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The Intrinsic Dimension of Images and Its Impact on Learning
generalization dimension manifold ImageNet CIFAR
It is widely believed that natural image data exhibits low-dimensional structure despite the high dimensionality of conventional pixel representations. This idea underlies a common intuition for the remarkable success of deep learning in computer vision. In this work, we apply dimension estimation tools to popular datasets and investigate the role of low-dimensional structure in deep learning. We find that common natural image datasets indeed have very low intrinsic dimension relative to the high number of pixels in the images. Additionally, we find that low dimensional datasets are easier for neural networks to learn, and models solving these tasks generalize better from training to test data. Along the way, we develop a technique for validating our dimension estimation tools on synthetic data generated by GANs allowing us to actively manipulate the intrinsic dimension by controlling the image generation process. Code for our experiments may be found \href{https://github.com/ppope/dimensions}{here}.
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Adjustable Real-time Style Transfer
Style transfer Generative models
Artistic style transfer is the problem of synthesizing an image with content similar to a given image and style similar to another. Although recent feed-forward neural networks can generate stylized images in real-time, these models produce a single stylization given a pair of style/content images, and the user doesn't have control over the synthesized output. Moreover, the style transfer depends on the hyper-parameters of the model with varying ``optimum" for different input images. Therefore, if the stylized output is not appealing to the user, she/he has to try multiple models or retrain one with different hyper-parameters to get a favorite stylization. In this paper, we address these issues by proposing a novel method which allows adjustment of crucial hyper-parameters, after the training and in real-time, through a set of manually adjustable parameters. These parameters enable the user to modify the synthesized outputs from the same pair of style/content images, in search of a favorite stylized image. Our quantitative and qualitative experiments indicate how adjusting these parameters is comparable to retraining the model with different hyper-parameters. We also demonstrate how these parameters can be randomized to generate results which are diverse but still very similar in style and content.
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Regioned Episodic Reinforcement Learning
Deep Reinforcement Learning Episodic Memory Sample Efficiency
Goal-oriented reinforcement learning algorithms are often good at exploration, not exploitation, while episodic algorithms excel at exploitation, not exploration. As a result, neither of these approaches alone can lead to a sample efficient algorithm in complex environments with high dimensional state space and delayed rewards. Motivated by these observations and shortcomings, in this paper, we introduce Regioned Episodic Reinforcement Learning (RERL) that combines the episodic and goal-oriented learning strengths and leads to a more sample efficient and ef- fective algorithm. RERL achieves this by decomposing the space into several sub-space regions and constructing regions that lead to more effective exploration and high values trajectories. Extensive experiments on various benchmark tasks show that RERL outperforms existing methods in terms of sample efficiency and final rewards.
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Filtered Inner Product Projection for Crosslingual Embedding Alignment
multilingual representations word embeddings natural language processing
Due to widespread interest in machine translation and transfer learning, there are numerous algorithms for mapping multiple embeddings to a shared representation space. Recently, these algorithms have been studied in the setting of bilingual lexicon induction where one seeks to align the embeddings of a source and a target language such that translated word pairs lie close to one another in a common representation space. In this paper, we propose a method, Filtered Inner Product Projection (FIPP), for mapping embeddings to a common representation space. As semantic shifts are pervasive across languages and domains, FIPP first identifies the common geometric structure in both embeddings and then, only on the common structure, aligns the Gram matrices of these embeddings. FIPP is applicable even when the source and target embeddings are of differing dimensionalities. Additionally, FIPP provides computational benefits in ease of implementation and is faster to compute than current approaches. Following the baselines in Glavas et al. 2019, we evaluate FIPP both in the context of bilingual lexicon induction and downstream language tasks. We show that FIPP outperforms existing methods on the XLING BLI dataset for most language pairs while also providing robust performance across downstream tasks.
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Designing realistic RL environment for power systems
Reinforcement Learning Power Systems Simulation
Power grids are critical infrastructure: ensuring they are reliable, robust and secure is essential to humanity,to everyday life, and to progress. With increasing renewable generation, growing electricity demand, and more severe weather events due to climate change, the task of maintaining efficient and robust power distribution poses a tremendous challenge to grid operators. In recent years, Reinforcement Learning (‘RL’) has shown substantial progress in solving highly complex, nonlinear problems, such as AlphaGo and it is now feasible that an RL agent could address the growing challenge of grid control.
Learning to Run a Power Network (‘L2RPN’) is one competition–organized by Réseau de Transport d’Electricité and
Electric Power Research Institute–aimed at testing out the capabilities of RL and other algorithms to safely control
electricity transportation in power grids. In 2020, L2RPN’s winners used a Semi Markov Afterstate Actor-Critic (‘SMAAC’)
approach to successfully manage a grid. L2RPN represents an important first step in commercializing AI for the power
grid, but additional refinement of the RL environment is necessary to make it realistic for application in the real world.
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NLP-EHUGBO: BRIDGING THE FAIRNESS GAP IN LANGUAGE MODELS FOR LOW-RESOURCE AFRICAN DIALECTS
low resource languages African Languages LLM for African languages Small Language Models
Despite advancements in language technologies, large language models (LLMs) continue to exclude low-resource languages, particularly African dialects like Ehugbo, a critically endangered variant of Igbo spoken by fewer than 150,000 people in Afikpo, Nigeria. Ehugbo’s linguistic complexity, featuring two additional alphabets beyond Igbo’s 36, exacerbates its marginalization, as existing models
fail to account for its unique structure. This exclusion perpetuates social and linguistic inequities, leaving speakers of such dialects without access to digital tools that could preserve their language and culture. This paper presents NLP-Ehugbo, a machine translation (MT) system designed to address this fairness gap. Using the only available parallel corpus, 1,021 Ehugbo-English sentences from the New Testament of the Bible, we evaluated and fine-tuned two state-of-the-art models, M2M100 (facebook/m2m100 418M) and NLLB (facebook/nllb-200-distilled-600M). Initial results were stark: M2M100 achieved a BLEU score of 1.2188, while NLLB scored only 0.0262. After fine-tuning, M2M100 improved to 16.1719, and NLLB achieved 20.4016, demonstrating the potential of adapting LLMs for low resource languages. Our findings reveal both promise and challenges. While fine-tuning significantly
improves performance, the lack of diverse datasets limits translation quality and reinforces the need for inclusive data collection practices. This work highlights the importance of community-driven approaches, as linguistic preservation cannot be achieved without the active involvement of native speakers. The significance of NLP-Ehugbo lies in its contribution to the fairness discourse in LLMs. By focusing on Ehugbo, we expose the systemic bias that excludes low-resource dialects and advocate for a more equitable approach to language technologies. This project not only advances the field of low-resource MT but also serves as a call to action for researchers and developers to prioritize linguistic
diversity, ensuring that no language is left behind in the digital age.
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Implicit Gradient Regularization
implicit regularization deep learning deep learning theory theoretical issues in deep learning theory regularization
Gradient descent can be surprisingly good at optimizing deep neural networks without overfitting and without explicit regularization. We find that the discrete steps of gradient descent implicitly regularize models by penalizing gradient descent trajectories that have large loss gradients. We call this Implicit Gradient Regularization (IGR) and we use backward error analysis to calculate the size of this regularization. We confirm empirically that implicit gradient regularization biases gradient descent toward flat minima, where test errors are small and solutions are robust to noisy parameter perturbations. Furthermore, we demonstrate that the implicit gradient regularization term can be used as an explicit regularizer, allowing us to control this gradient regularization directly. More broadly, our work indicates that backward error analysis is a useful theoretical approach to the perennial question of how learning rate, model size, and parameter regularization interact to determine the properties of overparameterized models optimized with gradient descent.
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Reinforcement Learning for Control with Probabilistic Stability Guarantee
control Lyapunov stability REINFORCE finite-sample bounds
Reinforcement learning is promising to control dynamical systems for which the traditional control methods are hardly applicable. However, in control theory, the stability of a closed-loop system can be hardly guaranteed using the policy/controller learned solely from samples. In this paper, we will combine Lyapunov's method in control theory and stochastic analysis to analyze the mean square stability of MDP in a model-free manner. Furthermore, the finite sample bounds on the probability of stability are derived as a function of the number M and length T of the sampled trajectories. And we show that there is a lower bound on T and the probability is much more demanding for M than T. Based on the theoretical results, a REINFORCE like algorithm is proposed to learn the controller and the Lyapunov function simultaneously.
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Deep Random Splines for Point Process Intensity Estimation
splines VAE random functions point processes neuroscience
Gaussian processes are the leading class of distributions on random functions, but they suffer from well known issues including difficulty scaling and inflexibility with respect to certain shape constraints (such as nonnegativity). Here we propose Deep Random Splines, a flexible class of random functions obtained by transforming Gaussian noise through a deep neural network whose output are the parameters of a spline. Unlike Gaussian processes, Deep Random Splines allow us to readily enforce shape constraints while inheriting the richness and tractability of deep generative models. We also present an observational model for point process data which uses Deep Random Splines to model the intensity function of each point process and apply it to neuroscience data to obtain a low-dimensional representation of spiking activity. Inference is performed via a variational autoencoder that uses a novel recurrent encoder architecture that can handle multiple point processes as input.
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Factorizing Declarative and Procedural Knowledge in Structured, Dynamical Environments
procedural knowledge declarative knowledge Systematicity
Modeling a structured, dynamic environment like a video game requires keeping track of the objects and their states (declarative knowledge) as well as predicting how objects behave (procedural knowledge). Black-box models with a monolithic hidden state often fail to apply procedural knowledge consistently and uniformly, i.e., they lack systematicity. For example, in a video game, correct prediction of one enemy's trajectory does not ensure correct prediction of another's. We address this issue via an architecture that factorizes declarative and procedural knowledge and that imposes modularity within each form of knowledge. The architecture consists of active modules called object files that maintain the state of a single object and invoke passive external knowledge sources called schemata that prescribe state updates. To use a video game as an illustration, two enemies of the same type will share schemata but will have separate object files to encode their distinct state (e.g., health, position). We propose to use attention to determine which object files to update, the selection of schemata, and the propagation of information between object files. The resulting architecture is a drop-in replacement conforming to the same input-output interface as normal recurrent networks (e.g., LSTM, GRU) yet achieves substantially better generalization on environments that have multiple object tokens of the same type, including a challenging intuitive physics benchmark.
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What to Prune and What Not to Prune at Initialization
Network Sparsity Machine Learning Initialization Pruning
Post-training dropout based approaches achieve high sparsity and are well established means of deciphering problems relating to computational cost and overfitting in Neural Network architectures. Contrastingly, pruning at initialization is still far behind. Initialization pruning is more efficacious when it comes to scaling computation cost of the network. Furthermore, it handles overfitting just as well as post training dropout. It is also averse to retraining losses.
In approbation of the above reasons, the paper presents two approaches to prune at initialization. The goal is to achieve higher sparsity while preserving performance. 1) K-starts, begins with k random p-sparse matrices at initialization. In the first couple of epochs the network then determines the "fittest" of these p-sparse matrices in an attempt to find the "lottery ticket" p-sparse network. The approach is adopted from how evolutionary algorithms find the best individual. Depending on the Neural Network architecture, fitness criteria can be based on magnitude of network weights, magnitude of gradient accumulation over an epoch or a combination of both. 2) Dissipating gradients approach, aims at eliminating weights that remain within a fraction of their initial value during the first couple of epochs. Removing weights in this manner despite their magnitude best preserves performance of the network. Contrarily, the approach also takes the most epochs to achieve higher sparsity. 3) Combination of dissipating gradients and kstarts outperforms either methods and random dropout consistently.
The benefits of using the provided pertaining approaches are: 1) They do not require specific knowledge of the classification task, fixing of dropout threshold or regularization parameters 2) Retraining of the model is neither necessary nor affects the performance of the p-sparse network.
We evaluate the efficacy of the said methods on Autoencoders and Fully Connected Multilayered Perceptrons. The datasets used are MNIST and Fashion MNIST.
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Generating Plannable Lifted Action Models for Visually Generated Logical Predicates
Object-Centric Representation Planning Discrete VAE
We propose FOSAE++, an unsupervised end-to-end neural system that generates a compact discrete state transition model (dynamics / action model) from raw visual observations. Our representation can be exported to Planning Domain Description Language (PDDL), allowing symbolic state-of-the-art classical planners to perform high-level task planning on raw observations. FOSAE++ expresses states and actions in First Order Logic (FOL), a superset of so-called object-centric representation. It is the first unsupervised neural system that fully supports FOL in PDDL action modeling, while existing systems are limited to continuous, propositional, or property-based representations, and/or require manually labeled input for actions/predicates/propositions.
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Disentangling Action Sequences: Discovering Correlated Samples
disentanglement representation learning unsupervised inductive bias
Disentanglement is a highly desirable property of representation due to its similarity with human’s understanding and reasoning. This improves interpretability, enables the performance of down-stream tasks, and enables controllable generative models.However, this domain is challenged by the abstract notion and incomplete theories to support unsupervised disentanglement learning. We demonstrate the data itself, such as the orientation of images, plays a crucial role in disentanglement and instead of the factors, and the disentangled representations align the latent variables with the action sequences. We further introduce the concept of disentangling action sequences which facilitates the description of the behaviours of the existing disentangling approaches. An analogy for this process is to discover the commonality between the things and categorizing them.
Furthermore, we analyze the inductive biases on the data and find that the latent information thresholds are correlated with the significance of the actions. For the supervised and unsupervised settings, we respectively introduce two methods to measure the thresholds. We further propose a novel framework, fractional variational autoencoder (FVAE), to disentangle the action sequences with different significance step-by-step. Experimental results on dSprites and 3D Chairs show that FVAE improves the stability of disentanglement.
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On The Effects of Learning Views on Neural Representations in Self-Supervised Learning
contrastive learning adverserial networks data augmentation
Contrastive self-supervised learning (SSL) methods require domain expertise to select a set of data transformations that work well on a specific downstream task, modality, and domain. This creates a bottleneck for the utilization of SSL strategies on new datasets, particularly those sampled from different domains. This blog post details the Viewmaker Networks, a method that attempts to overcome this issue by using constrained perturbations and an adversarial training objective to synthesize novel views. This method can be broadly extended to many domains, as it learns views directly from the training data. We cover the details of the methodology and related concepts as they apply to contrastive visual representation learning. This blog post also develops further insights into the visual representations learned using the Viewmaker's augmented views by conducting additional experiments. Specifically, we investigate the dimensional collapse and representational similarity between differently trained models (handcrafted views and viewmaker views). We observe that training models using Viewmaker views not only make better use of the embedding space but also learn a different function of the data when compared to training models using handcrafted views, something one should carefully consider when choosing one over the other. We feel these observations will encourage further discussions on learned views in self-supervised learning.
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Efficient Empowerment Estimation for Unsupervised Stabilization
unsupervised stabilization representation of dynamical systems neural networks empowerment intrinsic motivation
Intrinsically motivated artificial agents learn advantageous behavior without externally-provided rewards. Previously, it was shown that maximizing mutual information between agent actuators and future states, known as the empowerment principle, enables unsupervised stabilization of dynamical systems at upright positions, which is a prototypical intrinsically motivated behavior for upright standing and walking. This follows from the coincidence between the objective of stabilization and the objective of empowerment. Unfortunately, sample-based estimation of this kind of mutual information is challenging. Recently, various variational lower bounds (VLBs) on empowerment have been proposed as solutions; however, they are often biased, unstable in training, and have high sample complexity. In this work, we propose an alternative solution based on a trainable representation of a dynamical system as a Gaussian channel, which allows us to efficiently calculate an unbiased estimator of empowerment by convex optimization. We demonstrate our solution for sample-based unsupervised stabilization on different dynamical control systems and show the advantages of our method by comparing it to the existing VLB approaches. Specifically, we show that our method has a lower sample complexity, is more stable in training, possesses the essential properties of the empowerment function, and allows estimation of empowerment from images. Consequently, our method opens a path to wider and easier adoption of empowerment for various applications.
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Detecting Hallucinated Content in Conditional Neural Sequence Generation
conditional text generation hallucination detection sequence generation evaluation neural machine translation abstractive text summarization
Neural sequence models can generate highly fluent sentences but recent studies have also shown that they are also prone to hallucinate additional content not supported by the input, which can cause a lack of trust in the model.
To better assess the faithfulness of the machine outputs, we propose a new task to predict whether each token in the output sequence is hallucinated conditioned on the source input, and collect new manually annotated evaluation sets for this task.
We also introduce a novel method for learning to model hallucination detection, based on pretrained language models fine tuned on synthetic data that includes automatically inserted hallucinations.
Experiments on machine translation and abstract text summarization demonstrate the effectiveness of our proposed approach -- we obtain an average F1 of around 0.6 across all the benchmark datasets.
Furthermore, we demonstrate how to use the token-level hallucination labels to define a fine-grained loss over the target sequence in the low-resource machine translation and achieve significant improvements over strong baseline methods.
We will also release our annotated data and code for future research.
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Learning without Forgetting: Task Aware Multitask Learning for Multi-Modality Tasks
Deep Learning Joint Learning Meta Learning Multi-task Learning
Existing joint learning strategies like multi-task or meta-learning focus more on shared learning and have little to no scope for task-specific learning. This creates the need for a distinct shared pretraining phase and a task-specific finetuning phase. The fine-tuning phase creates separate systems for each task, where improving the performance of a particular task necessitates forgetting some of the knowledge garnered in other tasks. Humans, on the other hand, perform task-specific learning in synergy with general domain-based learning. Inspired by these learning patterns in humans, we suggest a simple yet generic task aware framework to incorporate into existing joint learning strategies. The proposed framework computes task-specific representations to modulate model parameters during joint learning. Hence, it performs both shared and task-specific learning in a single-phase resulting in a single model for all the tasks. The single model itself achieves significant performance gains over the existing joint learning strategies. For example, we train a model on Speech Translation (ST), Automatic Speech Recognition (ASR), and Machine Translation (MT) tasks using the proposed task aware joint learning approach. This single model achieves a performance of 28.64 BLEU score on ST MuST-C English-German, WER of 11.61 on ASR TEDLium v3, and BLEU score of 23.35 on MT WMT14 English-German tasks. This sets a new state-of-the-art performance (SOTA) on the ST task while outperforming the existing end-to-end ASR systems with a competitive performance on the MT task.
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Monte-Carlo Planning and Learning with Language Action Value Estimates
natural language processing Monte-Carlo tree search reinforcement learning interactive fiction
Interactive Fiction (IF) games provide a useful testbed for language-based reinforcement learning agents, posing significant challenges of natural language understanding, commonsense reasoning, and non-myopic planning in the combinatorial search space. Agents based on standard planning algorithms struggle to play IF games due to the massive search space of language actions. Thus, language-grounded planning is a key ability of such agents, since inferring the consequence of language action based on semantic understanding can drastically improve search. In this paper, we introduce Monte-Carlo planning with Language Action Value Estimates (MC-LAVE) that combines a Monte-Carlo tree search with language-driven exploration. MC-LAVE invests more search effort into semantically promising language actions using locally optimistic language value estimates, yielding a significant reduction in the effective search space of language actions. We then present a reinforcement learning approach via MC-LAVE, which alternates between MC-LAVE planning and supervised learning of the self-generated language actions. In the experiments, we demonstrate that our method achieves new high scores in various IF games.
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Latent Adversarial Training Improves the Representation of Refusal
Latent Adversarial Training refusal behavior refusal direction ablation adversarial robustness
Recent work has shown that language models' refusal behavior is primarily encoded in a single direction in their latent space, making it vulnerable to targeted attacks. While Latent Adversarial Training (LAT) attempts to improve robustness by introducing noise during training, a key question remains: How does this noise-based training affect the underlying representation of refusal behavior? Understanding this encoding is crucial for evaluating LAT's effectiveness and limitations, just as the discovery of linear refusal directions revealed vulnerabilities in traditional supervised safety fine-tuning (SSFT).
Through the analysis of Llama 2 7B, we examine how LAT reorganizes the refusal behavior in the model's latent space compared to SSFT and embedding space adversarial training (AT). By computing activation differences between harmful and harmless instruction pairs and applying Singular Value Decomposition (SVD), we find that LAT significantly alters the refusal representation, concentrating it in the first two SVD components which explain approximately 75% of the activation differences variance—significantly higher than in reference models. This concentrated representation leads to more effective and transferable refusal vectors for ablation attacks: LAT models show improved robustness when attacked with vectors from reference models but become more vulnerable to self-generated vectors compared to SSFT and AT. Our findings suggest that LAT's training perturbations enable a more comprehensive representation of refusal behavior, highlighting both its potential strengths and vulnerabilities for improving model safety.
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Real-time Uncertainty Decomposition for Online Learning Control
uncertainty decomposition epistemic uncertainty online learning real-time control
Safety-critical decisions based on machine learning models require a clear understanding of the involved uncertainties to avoid hazardous or risky situations. While aleatoric uncertainty can be explicitly modeled given a parametric description, epistemic uncertainty rather describes the presents or absence of training data. This paper proposes a novel generic method for modeling
epistemic uncertainty and shows its advantages over existing approaches for neural networks on various data sets. It can be directly combined with aleatoric uncertainty estimates and
allows for prediction in real-time as the inference is sample-free. We exploit this property in a model-based quadcopter control setting and demonstrate how the controller benefits from a differentiation between aleatoric and epistemic uncertainty in online learning of thermal disturbances.
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Adaptive and Generative Zero-Shot Learning
Generalized zero-shot learning mixup
We address the problem of generalized zero-shot learning (GZSL) where the task is to predict the class label of a target image whether its label belongs to the seen or unseen category. Similar to ZSL, the learning setting assumes that all class-level semantic features are given, while only the images of seen classes are available for training. By exploring the correlation between image features and the corresponding semantic features, the main idea of the proposed approach is to enrich the semantic-to-visual (S2V) embeddings via a seamless fusion of adaptive and generative learning. To this end, we extend the semantic features of each class by supplementing image-adaptive attention so that the learned S2V embedding can account for not only inter-class but also intra-class variations. In addition, to break the limit of training with images only from seen classes, we design a generative scheme to simultaneously generate virtual class labels and their visual features by sampling and interpolating over seen counterparts. In inference, a testing image will give rise to two different S2V embeddings, seen and virtual. The former is used to decide whether the underlying label is of the unseen category or otherwise a specific seen class; the latter is to predict an unseen class label. To demonstrate the effectiveness of our method, we report state-of-the-art results on four standard GZSL datasets, including an ablation study of the proposed modules.
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Attention Based Joint Learning for Supervised Electrocardiogram Arrhythmia Differentiation with Unsupervised Abnormal Beat Segmentation
interpretability multitask learning attention mechanism electrocardiography
Deep learning has shown great promise in arrhythmia classification in electrocar- diogram (ECG). Existing works, when classifying an ECG segment with multiple beats, do not identify the locations of the anomalies, which reduces clinical inter- pretability. On the other hand, segmenting abnormal beats by deep learning usu- ally requires annotation for a large number of regular and irregular beats, which can be laborious, sometimes even challenging, with strong inter-observer variabil- ity between experts. In this work, we propose a method capable of not only dif- ferentiating arrhythmia but also segmenting the associated abnormal beats in the ECG segment. The only annotation used in the training is the type of abnormal beats and no segmentation labels are needed. Imitating human’s perception of an ECG signal, the framework consists of a segmenter and classifier. The segmenter outputs an attention map, which aims to highlight the abnormal sections in the ECG by element-wise modulation. Afterwards, the signals are sent to a classifier for arrhythmia differentiation. Though the training data is only labeled to super- vise the classifier, the segmenter and the classifier are trained in an end-to-end manner so that optimizing classification performance also adjusts how the abnor- mal beats are segmented. Validation of our method is conducted on two dataset. We observe that involving the unsupervised segmentation in fact boosts the clas- sification performance. Meanwhile, a grade study performed by experts suggests that the segmenter also achieves satisfactory quality in identifying abnormal beats, which significantly enhances the interpretability of the classification results.
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PINA: a PyTorch Framework for Solving Differential Equations by Deep Learning for Research and Production Environments
PINNs Neural Operators PDEs ROMs
We present a versatile software designed for solving differential equations employing neural networks. The package is called PINA, an open-source Python library built upon the robust foundations of PyTorch and Lightning. It allows end-users to formulate their problem and craft their models to effortlessly compute solutions of PDEs by Physics Informed Neural Networks and Neural Operators. The modular structure of PINA permits it to adapt for user specifics, thus offering the freedom to select the most suitable learning techniques for their particular problem domain. Furthermore, by leveraging the capabilities of the Lightning package, PINA adapts to various hardware setups, including GPUs and TPUs. This adaptability positions PINA as an ideal candidate for the transition of these methodologies into production and industrial pipelines, where computational efficiency and scalability are of paramount importance.
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Oracle Guided Image Synthesis with Relative Queries
Variational Autoencoder Image Synthesis Relative Attributes Paired Comparisons Triplet Loss Structured latent representations Human-Computer Interaction
Isolating and controlling specific features in the outputs of generative models in a user-friendly way is a difficult and open-ended problem. We develop techniques that allow a user to generate an image they are envisioning in their head by answering a sequence of relative queries of the form \textit{``do you prefer image $a$ or image $b$?''} Our framework consists of a Conditional VAE that uses the collected relative queries to partition the latent space into preference-relevant features and non-preference-relevant features. We then use the user's responses to relative queries to determine the preference-relevant features that correspond to their envisioned output image. Additionally, we develop techniques for modeling the uncertainty in images' predicted preference-relevant features, allowing our framework to generalize to scenarios in which the relative query training set contains noise.
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Emergent Symbols through Binding in External Memory
abstract rules out-of-distribution generalization external memory indirection variable binding
A key aspect of human intelligence is the ability to infer abstract rules directly from high-dimensional sensory data, and to do so given only a limited amount of training experience. Deep neural network algorithms have proven to be a powerful tool for learning directly from high-dimensional data, but currently lack this capacity for data-efficient induction of abstract rules, leading some to argue that symbol-processing mechanisms will be necessary to account for this capacity. In this work, we take a step toward bridging this gap by introducing the Emergent Symbol Binding Network (ESBN), a recurrent network augmented with an external memory that enables a form of variable-binding and indirection. This binding mechanism allows symbol-like representations to emerge through the learning process without the need to explicitly incorporate symbol-processing machinery, enabling the ESBN to learn rules in a manner that is abstracted away from the particular entities to which those rules apply. Across a series of tasks, we show that this architecture displays nearly perfect generalization of learned rules to novel entities given only a limited number of training examples, and outperforms a number of other competitive neural network architectures.
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Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning
task gem active reward active reward learning work imitation object rearrangement tasks ai agent needs single expert demonstration
In this work, we consider one-shot imitation learning for object rearrangement tasks, where an AI agent needs to watch a single expert demonstration and learn to perform the same task in different environments. To achieve a strong generalization, the AI agent must infer the spatial goal specification for the task. However, there can be multiple goal specifications that fit the given demonstration. To address this, we propose a reward learning approach, Graph-based Equivalence Mappings (GEM), that can discover spatial goal representations that are aligned with the intended goal specification, enabling successful generalization in unseen environments. We conducted experiments with simulated oracles and with human subjects. The results show that GEM can drastically improve the generalizability of the learned goal representations over strong baselines.
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Inferring Principal Components in the Simplex with Multinomial Variational Autoencoders
Multinomial variational autoencoders variational autoencoders ILR transform compositional PCA probabilistic PCA multinomial logistic normal
Covariance estimation on high-dimensional data is a central challenge across multiple scientific disciplines. Sparse high-dimensional count data, frequently encountered in biological applications such as DNA sequencing and proteomics, are often well modeled using multinomial logistic normal models. In many cases, these datasets are also compositional, presented item-wise as fractions of a normalized total, due to measurement and instrument constraints. In compositional settings, three key factors limit the ability of these models to estimate covariance: (1) the computational complexity of inverting high-dimensional covariance matrices, (2) the non-exchangeability introduced from the summation constraint on multinomial parameters, and (3) the irreducibility of the component multinomial logistic normal distribution that necessitates the use of parameter augmentation, or similar techniques, during inference. We show that a variational autoencoder augmented with a fast isometric log-ratio (ILR) transform can address these issues and accurately estimate principal components from multinomially logistic normal distributed data.
This model can be optimized on GPUs and modified to handle mini-batching, with the ability to scale across thousands of dimensions and thousands of samples.
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Object Representations as Fixed Points: Training Iterative Inference Algorithms with Implicit Differentiation
implicit differentiation object-centric learning iterative amortized inference
Deep generative models, particularly those that aim to factorize the observations into discrete entities (such as objects), must often use iterative inference procedures that break symmetries among equally plausible explanations for the data. Such inference procedures include variants of the expectation-maximization algorithm and structurally resemble clustering algorithms in a latent space. However, combining such methods with deep neural networks necessitates differentiating through the inference process, which can make optimization exceptionally challenging. We observe that such iterative amortized inference methods can be made differentiable by means of the implicit function theorem, and develop an implicit differentiation approach that improves the stability and tractability of training such models by decoupling the forward and backward passes. This connection enables us to apply recent advances in optimizing implicit layers to not only improve the stability and optimization of the slot attention module in SLATE, a state-of-the-art method for learning entity representations, but do so with constant space and time complexity in backpropagation and only one additional line of code.
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Reproducing Meta-learning with differentiable closed-form solvers
reproducibility meta-learning closed-form few-shot miniimagenet cifar-fs deep learning
In this paper, we present a reproduction of the paper of Bertinetto et al. [2019] "Meta-learning with differentiable closed-form solvers" as part of the ICLR 2019 Reproducibility Challenge. In successfully reproducing the most crucial part of the paper, we reach a performance that is comparable with or superior to the original paper on two benchmarks for several settings. We evaluate new baseline results, using a new dataset presented in the paper. Yet, we also provide multiple remarks and recommendations about reproducibility and comparability. After we brought our reproducibility work to the authors’ attention, they have updated the original paper on which this work is based and released code as well. Our contributions mainly consist in reproducing the most important results of their original paper, in giving insight in the reproducibility and in providing a first open-source implementation.
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Mitigating Mode Collapse by Sidestepping Catastrophic Forgetting
mode collapse catastrophic forgetting multi-adversarial training
Generative Adversarial Networks (GANs) are a class of generative models used for various applications, but they have been known to suffer from the mode collapse problem, in which some modes of the target distribution are ignored by the generator. Investigative study using a new data generation procedure indicates that the mode collapse of the generator is driven by the discriminator’s inability to maintain classification accuracy on previously seen samples, a phenomenon called Catastrophic Forgetting in continual learning. Motivated by this observation, we introduce a novel training procedure that dynamically spawns additional dis-criminators to remember previous modes of generation. On several datasets, we show that our training scheme can be plugged-in to existing GAN frameworks to mitigate mode collapse and improve standard metrics for GAN evaluation.
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Enhancing experimental signals in single-cell RNA-sequencing data using graph signal processing
computational biology graph signal processing genomics
Single-cell RNA-sequencing (scRNA-seq) is a powerful tool for analyzing biological systems. However, due to biological and technical noise, quantifying the effects of multiple experimental conditions presents an analytical challenge. To overcome this challenge, we developed MELD: Manifold Enhancement of Latent Dimensions. MELD leverages tools from graph signal processing to learn a latent dimension within the data scoring the prototypicality of each datapoint with respect to experimental or control conditions. We call this dimension the Enhanced Experimental Signal (EES). MELD learns the EES by filtering the noisy categorical experimental label in the graph frequency domain to recover a smooth signal with continuous values. This method can be used to identify signature genes that vary between conditions and identify which cell types are most affected by a given perturbation. We demonstrate the advantages of MELD analysis in two biological datasets, including T-cell activation in response to antibody-coated beads and treatment of human pancreatic islet cells with interferon gamma.
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Cut out the annotator, keep the cutout: better segmentation with weak supervision
Weak supervision segmentation CNN latent variable medical imaging
Constructing large, labeled training datasets for segmentation models is an expensive and labor-intensive process. This is a common challenge in machine learning, addressed by methods that require few or no labeled data points such as few-shot learning (FSL) and weakly-supervised learning (WS). Such techniques, however, have limitations when applied to image segmentation---FSL methods often produce noisy results and are strongly dependent on which few datapoints are labeled, while WS models struggle to fully exploit rich image information. We propose a framework that fuses FSL and WS for segmentation tasks, enabling users to train high-performing segmentation networks with very few hand-labeled training points. We use FSL models as weak sources in a WS framework, requiring a very small set of reference labeled images, and introduce a new WS model that focuses on key areas---areas with contention among noisy labels---of the image to fuse these weak sources. Empirically, we evaluate our proposed approach over seven well-motivated segmentation tasks. We show that our methods can achieve within 1.4 Dice points compared to fully supervised networks while only requiring five hand-labeled training points. Compared to existing FSL methods, our approach improves performance by a mean 3.6 Dice points over the next-best method.
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Isometric Autoencoders
manifold learning autoencoders
High dimensional data is often assumed to be concentrated on or near a low-dimensional manifold. Autoencoders (AE) is a popular technique to learn representations of such data by pushing it through a neural network with a low dimension bottleneck while minimizing a reconstruction error. Using high capacity AE often leads to a large collection of minimizers, many of which represent a low dimensional manifold that fits the data well but generalizes poorly.
Two sources of bad generalization are: extrinsic, where the learned manifold possesses extraneous parts that are far from the data; and intrinsic, where the encoder and decoder introduce arbitrary distortion in the low dimensional parameterization. An approach taken to alleviate these issues is to add a regularizer that favors a particular solution; common regularizers promote sparsity, small derivatives, or robustness to noise.
In this paper, we advocate an isometry (i.e., local distance preserving) regularizer. Specifically, our regularizer encourages: (i) the decoder to be an isometry; and (ii) the encoder to be the decoder’s pseudo-inverse, that is, the encoder extends the inverse of the decoder to the ambient space by orthogonal projection. In a nutshell, (i) and (ii) fix both intrinsic and extrinsic degrees of freedom and provide a non-linear generalization to principal component analysis (PCA). Experimenting with the isometry regularizer on dimensionality reduction tasks produces useful low-dimensional data representations.
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Regularity Normalization: Constraining Implicit Space with Minimum Description Length
MDL Universal code LLD Normalization Biological plausibility Unsupervised attention Imbalanced data
Inspired by the adaptation phenomenon of biological neuronal firing, we propose regularity normalization: a reparameterization of the activation in the neural network that take into account the statistical regularity in the implicit space. By considering the neural network optimization process as a model selection problem, the implicit space is constrained by the normalizing factor, the minimum description length of the optimal universal code. We introduce an incremental version of computing this universal code as normalized maximum likelihood and demonstrated its flexibility to include data prior such as top-down attention and other oracle information and its compatibility to be incorporated into batch normalization and layer normalization. The preliminary results showed that the proposed method outperforms existing normalization methods in tackling the limited and imbalanced data from a non-stationary distribution benchmarked on computer vision task. As an unsupervised attention mechanism given input data, this biologically plausible normalization has the potential to deal with other complicated real-world scenarios as well as reinforcement learning setting where the rewards are sparse and non-uniform. Further research is proposed to discover these scenarios and explore the behaviors among different variants.
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Semi-supervised counterfactual explanations
Semi-supervised feature representation counterfactual explanations
Counterfactual explanations for machine learning models are used to find minimal interventions to the feature values such that the model changes the prediction to a different output or a target output. A valid counterfactual explanation should have likely feature values. Here, we address the challenge of generating counterfactual explanations that lie in the same data distribution as that of the training data and more importantly, they belong to the target class distribution. This requirement has been addressed through the incorporation of auto-encoder reconstruction loss in the counterfactual search process. Connecting the output behavior of the classifier to the latent space of the auto-encoder has further improved the speed of the counterfactual search process and the interpretability of the resulting counterfactual explanations. Continuing this line of research, we show further improvement in the interpretability of counterfactual explanations when the auto-encoder is trained in a semi-supervised fashion with class tagged input data. We empirically evaluate our approach on several datasets and show considerable improvement in-terms of several metrics.
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Speeding up Deep Learning Training by Sharing Weights and Then Unsharing
fast training BERT transformer weight sharing deep learning
It has been widely observed that increasing deep learning model sizes often leads to significant performance improvements on a variety of natural language processing and computer vision tasks. In the meantime, however, computational costs and training time would dramatically increase when models get larger. In this paper, we propose a simple approach to speed up training for a particular kind of deep networks which contain repeated structures, such as the transformer module. In our method, we first train such a deep network with the weights shared across all the repeated layers till some point. We then stop weight sharing and continue training until convergence. The untying point is automatically determined by monitoring gradient statistics. Our adaptive untying criterion is obtained from a theoretic analysis over deep linear networks. Empirical results show that our method is able to reduce the training time of BERT by 50%.
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FCDD - Explainable Anomaly Detection
anomaly-detection explainability one-class classification
Anomaly detection (AD) is the task of identifying anomalies in a corpus of data. There are many real-life applications where we find anomalies including applications in healthcare and manufacturing. We briefly discuss some of the existing approaches for solving this problem before focusing on one specific approach. FCDD, an approach introduced in a previous ICLR paper , provides an explainable way to solve anomaly detection, while providing SOTA performance. In this blog, we go over the paper and discuss the advantages and disadvantages of the methods involved in it. We also review the various additional experiments from the authors of the paper which were included in the appendix to the main paper.
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Hard Masking for Explaining Graph Neural Networks
Interpretability Graph Neural Networks Hard Masks
Graph Neural Networks (GNNs) are a flexible and powerful family of models that build nodes' representations on irregular graph-structured data. This paper focuses on explaining or interpreting the rationale underlying a given prediction of already trained graph neural networks for the node classification task. Existing approaches for interpreting GNNs try to find subsets of important features and nodes by learning a continuous mask. Our objective is to find discrete masks that are arguably more interpretable while minimizing the expected deviation from the underlying model's prediction. We empirically show that our explanations are both more predictive and sparse. Additionally, we find that multiple diverse explanations are possible, which sufficiently explain a prediction. Finally, we analyze the explanations to find the effect of network homophily on the decision-making process of GNNs.
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Learning Entity Representations for Few-Shot Reconstruction of Wikipedia Categories
representation learning entities few-shot Wikipedia
Language modeling tasks, in which words are predicted on the basis of a local context, have been very effective for learning word embeddings and context dependent representations of phrases. Motivated by the observation that efforts to code
world knowledge into machine readable knowledge bases tend to be entity-centric,
we investigate the use of a fill-in-the-blank task to learn context independent representations of entities from the contexts in which those entities were mentioned.
We show that large scale training of neural models allows us to learn extremely high fidelity entity typing information, which we demonstrate with few-shot reconstruction of Wikipedia categories. Our learning approach is powerful enough
to encode specialized topics such as Giro d’Italia cyclists.
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On Single-environment Extrapolations in Graph Classification and Regression Tasks
Extrapolation Graphs GNNs SCM Causality Counterfactual Inference
Extrapolation in graph classification/regression remains an underexplored area of an otherwise rapidly developing field. Our work contributes to a growing literature by providing the first systematic counterfactual modeling framework for extrapolations in graph classification/regression tasks. To show that extrapolation from a single training environment is possible, we develop a connection between certain extrapolation tasks on graph sizes and Lovasz's characterization of graph limits. For these extrapolations, standard graph neural networks (GNNs) will fail, while classifiers using induced homomorphism densities succeed, but mostly on unattributed graphs. Generalizing these density features through a GNN subgraph decomposition allows them to also succeed in more complex attributed graph extrapolation tasks. Finally, our experiments validate our theoretical results and showcase some shortcomings of common (interpolation) methods in the literature.
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TRUTH DECAY: Quantifying Multi-Turn Sycophancy in Language Models
LLM Sycophancy Multi-step Dialogue Mimicry Static Feedback Answer Sycophancy
Rapid improvements in large language models have unveiled a critical challenge in human-AI interaction: sycophancy. In this context, sycophancy refers to the tendency of models to excessively agree with or flatter users, often at the expense of factual accuracy. While previous studies have primarily analyzed this behavior in single-turn interactions, its persistence and evolution in multi-step conversations remain largely unexplored. We introduce TRUTH DECAY, a benchmark specifically designed to evaluate sycophancy in extended dialogues, where language models must navigate iterative user feedback, challenges, and persuasion. We prompt models to elicit four types of sycophantic biases. We then propose and test sycophancy reduction strategies, evaluating their effectiveness beyond single-step interactions.
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Provable Rich Observation Reinforcement Learning with Combinatorial Latent States
Reinforcement learning theory Rich observation Noise-contrastive learning State abstraction Factored MDP
We propose a novel setting for reinforcement learning that combines two common real-world difficulties: presence of observations (such as camera images) and factored states (such as location of objects). In our setting, the agent receives observations generated stochastically from a "latent" factored state. These observations are "rich enough" to enable decoding of the latent state and remove partial observability concerns. Since the latent state is combinatorial, the size of state space is exponential in the number of latent factors. We create a learning algorithm FactoRL (Fact-o-Rel) for this setting, which uses noise-contrastive learning to identify latent structures in emission processes and discover a factorized state space. We derive polynomial sample complexity guarantees for FactoRL which polynomially depend upon the number factors, and very weakly depend on the size of the observation space. We also provide a guarantee of polynomial time complexity when given access to an efficient planning algorithm.
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Learning to control the pitch of speech signals in the latent representation of a variational autoencoder
Deep generative models variational autoencoder (VAE) speech processing representation learning controlling the pitch of speech
Understanding and controlling latent representations in deep generative models is a challenging yet important problem for analyzing, transforming and generating various types of data. Speech signals are produced from a few physically meaningful continuous latent factors governed by the anatomical mechanisms of phonation. Among these factors, the fundamental frequency is of primary importance as it characterizes the pitch of the voice, which is an important feature of the prosody. In this work, from a variational autoencoder (VAE) trained in an unsupervised fashion on hours of natural speech signals and a few seconds of labeled speech generated with an artificial synthesizer, we propose a weakly-supervised method to (i) identify the latent subspace of the VAE that only encodes the pitch and (ii) learn how to move into this subspace so as to precisely control the fundamental frequency. The proposed approach is applied to the transformation of speech signals and compared experimentally with traditional signal processing methods and a VAE baseline.
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Key Protected Classification for GAN Attack Resilient Collaborative Learning
privacy preserving deep learning collaborative learning adversarial attack
Large-scale publicly available datasets play a fundamental role in training deep learning models. However, large-scale
datasets are difficult to collect in problems that involve processing of sensitive information.
Collaborative learning techniques provide a privacy-preserving solution in such cases, by enabling
training over a number of private datasets that are not shared by their owners.
Existing collaborative learning
techniques, combined with differential privacy, are shown to be resilient against a passive
adversary which tries to infer the training data only from the model parameters. However, recently, it has
been shown that the existing collaborative learning techniques are vulnerable to an active adversary that runs a GAN
attack during the learning phase. In this work, we propose a novel key-based collaborative learning technique that is
resilient against such GAN attacks. For this purpose, we present a collaborative learning formulation in which class scores
are protected by class-specific keys, and therefore, prevents a GAN attack. We also show that
very high dimensional class-specific keys can be utilized to improve robustness against attacks, without increasing the model complexity.
Our experimental results on two popular datasets, MNIST and AT&T Olivetti Faces, demonstrate the effectiveness of the proposed technique
against the GAN attack. To the best of our knowledge, the proposed approach is the first collaborative learning
formulation that effectively tackles an active adversary, and, unlike model corruption or differential privacy formulations,
our approach does not inherently feature a trade-off between model accuracy and data privacy.
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Variational Deterministic Uncertainty Quantification
Uncertainty estimation gaussian processes deep learning variational inference
Building on recent advances in uncertainty quantification using a single deep deterministic model (DUQ), we introduce variational Deterministic Uncertainty Quantification (vDUQ). We overcome several shortcomings of DUQ by recasting it as a Gaussian process (GP) approximation. Our principled approximation is based on an inducing point GP in combination with Deep Kernel Learning. This enables vDUQ to use rigorous probabilistic foundations, and work not only on classification but also on regression problems. We avoid uncertainty collapse away from the training data by regularizing the spectral norm of the deep feature extractor. Our method matches SotA accuracy, 96.2\% on CIFAR-10, while maintaining the speed of softmax models, and provides uncertainty estimates competitive with Deep Ensembles. We demonstrate our method in regression problems and by estimating uncertainty in causal inference for personalized medicine
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Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth
Representation learning
A key factor in the success of deep neural networks is the ability to scale models to improve performance by varying the architecture depth and width. This simple property of neural network design has resulted in highly effective architectures for a variety of tasks. Nevertheless, there is limited understanding of effects of depth and width on the learned representations. In this paper, we study this fundamental question. We begin by investigating how varying depth and width affects model hidden representations, finding a characteristic block structure in the hidden representations of larger capacity (wider or deeper) models. We demonstrate that this block structure arises when model capacity is large relative to the size of the training set, and is indicative of the underlying layers preserving and propagating the dominant principal component of their representations. This discovery has important ramifications for features learned by different models, namely, representations outside the block structure are often similar across architectures with varying widths and depths, but the block structure is unique to each model. We analyze the output predictions of different model architectures, finding that even when the overall accuracy is similar, wide and deep models exhibit distinctive error patterns and variations across classes.
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Learning to generate Wasserstein barycenters
Wasserstein barycenters Optimal Transport
Optimal transport is a notoriously difficult problem to solve numerically, with current approaches often remaining intractable for very large scale applications such as those encountered in machine learning. Wasserstein barycenters -- the problem of finding measures in-between given input measures in the optimal transport sense -- is even more computationally demanding.
By training a deep convolutional neural network, we improve by a factor of 60 the computational speed of Wasserstein barycenters over the fastest state-of-the-art approach on the GPU, resulting in milliseconds computational times on $512\times512$ regular grids.
We show that our network, trained on Wasserstein barycenters of pairs of measures, generalizes well to the problem of finding Wasserstein barycenters of more than two measures. We validate our approach on synthetic shapes generated via Constructive Solid Geometry as well as on the ``Quick, Draw'' sketches dataset.
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Outlier Preserving Distribution Mapping Autoencoders
outliers inliers distribution autoencoders latent space regions novel insight outlier
State-of-the-art deep outlier detection methods map data into a latent space with the aim of having outliers far away from inliers in this space. Unfortunately, this often fails as the divergence penalty they adopt pushes outliers into the same high-probability regions as inliers. We propose a novel method, OP-DMA, that successfully addresses the above problem. OP-DMA succeeds in mapping outliers to low probability regions in the latent space by leveraging a novel Prior-Weighted Loss (PWL) that utilizes the insight that outliers are likely to have a higher reconstruction error than inliers. Building on this insight, OP-DMA weights the reconstruction error of individual points by a multivariate Gaussian probability density function evaluated at each point's latent representation. We demonstrate and provide theoretical proof that this succeeds to map outliers to low-probability regions. Our experimental study shows that OP-DMA consistently outperforms state-of-art methods on a rich variety of outlier detection benchmark datasets.
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CausalAF: Causal Autoregressive Flow for Safety-Critical Scenes Generation
Causal Generative Models Safety-critical Scene Generation Autonomous driving
Goal-directed generation, aiming for solving downstream tasks by generating diverse data, has a potentially wide range of applications in the real world. Previous works tend to formulate goal-directed generation as a purely data-driven problem, which directly approximates the distribution of samples satisfying the goal. However, the generation ability of preexisting work is heavily restricted by inefficient sampling, especially for sparse goals that rarely show up in off-the-shelf datasets. For instance, generating safety-critical traffic scenes with the goal of increasing the risk of collision is critical to evaluate autonomous vehicles, but the rareness of such scenes is the biggest resistance.
In this paper, we integrate causality as a prior into the safety-critical scene generation process and propose a flow-based generative framework -- Causal Autoregressive Flow (CausalAF). CausalAF encourages the generative model to uncover and follow the causal relationship among generated objects via novel causal masking operations instead of searching the sample only from observational data. Extensive experiments on three heterogeneous traffic scenes illustrate that \textit{CausalAF} requires much fewer optimization resources to effectively generate goal-directed scenes for safety evaluation tasks.
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Globetrotter: Unsupervised Multilingual Translation from Visual Alignment
cross-modal multilingual unsupervised translation visual similarity
Machine translation in a multi-language scenario requires large-scale parallel corpora for every language pair. Unsupervised translation is challenging because there is no explicit connection between languages, and the existing methods have to rely on topological properties of the language representations. We introduce a framework that leverages visual similarity to align multiple languages, using images as the bridge between them. We estimate the cross-modal alignment between language and images, and use this estimate to guide the learning of cross-lingual representations. Our language representations are trained jointly in one model with a single stage. Experiments with fifty-two languages show that our method outperforms prior work on unsupervised word-level and sentence-level translation using retrieval.
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Recursive Binary Neural Network Learning Model with 2-bit/weight Storage Requirement
model data storage weights training less test error storage requirement learning model
This paper presents a storage-efficient learning model titled Recursive Binary Neural Networks for embedded and mobile devices having a limited amount of on-chip data storage such as hundreds of kilo-Bytes. The main idea of the proposed model is to recursively recycle data storage of weights (parameters) during training. This enables a device with a given storage constraint to train and instantiate a neural network classifier with a larger number of weights on a chip, achieving better classification accuracy. Such efficient use of on-chip storage reduces off-chip storage accesses, improving energy-efficiency and speed of training. We verified the proposed training model with deep and convolutional neural network classifiers on the MNIST and voice activity detection benchmarks. For the deep neural network, our model achieves data storage requirement of as low as 2 bits/weight, whereas the conventional binary neural network learning models require data storage of 8 to 32 bits/weight. With the same amount of data storage, our model can train a bigger network having more weights, achieving 1% less test error than the conventional binary neural network learning model. To achieve the similar classification error, the conventional binary neural network model requires 4× more data storage for weights than our proposed model. For the convolution neural network classifier, the proposed model achieves 2.4% less test error for the same on-chip storage or 6× storage savings to achieve the similar accuracy.
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Efficient Subgraph Rule Induction via Tree Folding in Differentiable Logic Programming
inductive logic programming subgraph rules gradient-based
Differentiable inductive logic programming techniques have proven effective at learning logic rules from noisy datasets; however, existing algorithms incur pernicious trade-offs between rule expressivity and scalability to large problems. Forward-chaining ILP algorithms can learn arbitrary rules, but their memory requirements scale exponentially with problem size. Backwards-chaining ILP algorithms address this limitation but do so with loss of generality by imposing the restrictive constraint that rules must be expressible as ensembles of independent chain-like Horn clauses. In this paper we present FUSE-ILP, a technique that relaxes this chain-like constraint and enables the differentiable evaluation of a restricted class of subgraph-like rules. Our method extends TensorLog-inspired backwards-chaining ILP techniques with branch masking and leaf grouping, which enable tree-like rule evaluation and “folding” of these trees into subgraphs. We demonstrate that this formulation allows our algorithm to learn more expressive rules than previous backwards-chaining algorithms while retaining a similar computational cost.
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Why Are Convolutional Nets More Sample-Efficient than Fully-Connected Nets?
sample complexity separation equivariance convolutional neural networks fully-connected
Convolutional neural networks often dominate fully-connected counterparts in generalization performance, especially on image classification tasks. This is often explained in terms of \textquotedblleft better inductive bias.\textquotedblright\ However, this has not been made mathematically rigorous, and the hurdle is that the sufficiently wide fully-connected net can always simulate the convolutional net. Thus the training algorithm plays a role. The current work describes a natural task on which a provable sample complexity gap can be shown, for standard training algorithms. We construct a single natural distribution on $\mathbb{R}^d\times\{\pm 1\}$ on which any orthogonal-invariant algorithm (i.e. fully-connected networks trained with most gradient-based methods from gaussian initialization) requires $\Omega(d^2)$ samples to generalize while $O(1)$ samples suffice for convolutional architectures. Furthermore, we demonstrate a single target function, learning which on all possible distributions leads to an $O(1)$ vs $\Omega(d^2/\varepsilon)$ gap. The proof relies on the fact that SGD on fully-connected network is orthogonal equivariant. Similar results are achieved for $\ell_2$ regression and adaptive training algorithms, e.g. Adam and AdaGrad, which are only permutation equivariant.
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Self-supervised Contrastive Zero to Few-shot Learning from Small, Long-tailed Text data
self-supervised pretraining zero-shot few-shot text-to-text contrastive self-supervised learning small data long-tail multi-label classification NLP
For natural language processing (NLP) ‘text-to-text’ tasks, prevailing approaches heavily rely on pretraining large self-supervised models on massive external datasources. However, this methodology is being critiqued for: exceptional compute and pretraining data requirements; diminishing returns on both large and small datasets; and importantly, favourable evaluation settings that overestimate performance differences. The core belief behind current methodology, coined 'the bitter lesson' by R. Sutton, is that 'compute scale-up beats data and compute-efficient algorithms', neglecting that progress in compute hardware scale-up is based almost entirely on the miniaturisation of resource consumption. We thus approach pretraining from a miniaturisation perspective, such as not to require massive external data sources and models, or learned translations from continuous input embeddings to discrete labels. To minimise overly favourable evaluation, we examine learning on a long-tailed, low-resource, multi-label text classification dataset with noisy, highly sparse labels and many rare concepts. To this end, we propose a novel 'dataset-internal' contrastive autoencoding approach to self-supervised pretraining and demonstrate marked improvements in zero-shot, few-shot and solely supervised learning performance; even under an unfavorable low-resource scenario, and without defaulting to large-scale external datasets for self-supervision. We also find empirical evidence that zero and few-shot learning markedly benefit from adding more 'dataset-internal', self-supervised training signals, which is of practical importance when retrieving or computing on large external sources of such signals is infeasible.
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Auction Learning as a Two-Player Game
Mechanism Design Auction Theory Game Theory Deep Learning
Designing an incentive compatible auction that maximizes expected revenue is a central problem in Auction Design. While theoretical approaches to the problem have hit some limits, a recent research direction initiated by Duetting et al. (2019) consists in building neural network architectures to find optimal auctions. We propose two conceptual deviations from their approach which result in enhanced performance. First, we use recent results in theoretical auction design to introduce a time-independent Lagrangian. This not only circumvents the need for an expensive hyper-parameter search (as in prior work), but also provides a single metric to compare the performance of two auctions (absent from prior work). Second, the optimization procedure in previous work uses an inner maximization loop to compute optimal misreports. We amortize this process through the introduction of an additional neural network. We demonstrate the effectiveness of our approach by learning competitive or strictly improved auctions compared to prior work. Both results together further imply a novel formulation of Auction Design as a two-player game with stationary utility functions.
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Point Cloud GAN
Point Cloud GAN
Generative Adversarial Networks (GAN) can achieve promising performance on learning complex data distributions on different types of data. In this paper, we first show that a straightforward extension of an existing GAN algorithm is not applicable to point clouds, because the constraint required for discriminators is undefined for set data. We propose a two fold modification to a GAN algorithm to be able to generate point clouds (PC-GAN). First, we combine ideas from hierarchical Bayesian modeling and implicit generative models by learning a hierarchical and interpretable sampling process. A key component of our method is that we train a posterior inference network for the hidden variables. Second, PC-GAN defines a generic framework that can incorporate many existing GAN algorithms. We further propose a sandwiching objective, which results in a tighter Wasserstein distance estimate than the commonly used dual form in WGAN. We validate our claims on the ModelNet40 benchmark dataset and observe that PC- GAN trained by the sandwiching objective achieves better results on test data than existing methods. We also conduct studies on several tasks, including generalization on unseen point clouds, latent space interpolation, classification, and image to point clouds transformation, to demonstrate the versatility of the proposed PC-GAN algorithm.
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What's new? Summarizing Contributions in Scientific Literature
abstractive summarization scientific papers
With thousands of academic articles shared on a daily basis, it has become increasingly difficult to keep up with the latest scientific findings. To overcome this problem, we introduce a new task of $\textit{disentangled paper summarization}$, which seeks to generate separate summaries for the paper contributions and the context of the work, making it easier to identify the key findings shared in articles. For this purpose, we extend the S2ORC corpus of academic articles, which spans a diverse set of domains ranging from economics to psychology, by adding disentangled "contribution" and "context" reference labels. Together with the dataset, we introduce and analyze three baseline approaches: 1) a unified model controlled by input code prefixes, 2) a model with separate generation heads specialized in generating the disentangled outputs, and 3) a training strategy that guides the model using additional supervision coming from inbound and outbound citations. We also propose a comprehensive automatic evaluation protocol which reports the $\textit{relevance}$, $\textit{novelty}$, and $\textit{disentanglement}$ of generated outputs. Through a human study involving expert annotators, we show that in 79%, of cases our new task is considered more helpful than traditional scientific paper summarization.
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Hopfield Networks is All You Need
Modern Hopfield Network Energy Attention Convergence Storage Capacity Hopfield layer Associative Memory
We introduce a modern Hopfield network with continuous states and a corresponding update rule. The new Hopfield network can store exponentially (with the dimension of the associative space) many patterns, retrieves the pattern with one update, and has exponentially small retrieval errors. It has three types of energy minima (fixed points of the update): (1) global fixed point averaging over all patterns, (2) metastable states averaging over a subset of patterns, and (3) fixed points which store a single pattern. The new update rule is equivalent to the attention mechanism used in transformers. This equivalence enables a characterization of the heads of transformer models. These heads perform in the first layers preferably global averaging and in higher layers partial averaging via metastable states. The new modern Hopfield network can be integrated into deep learning architectures as layers to allow the storage of and access to raw input data, intermediate results, or learned prototypes.
These Hopfield layers enable new ways of deep learning, beyond fully-connected, convolutional, or recurrent networks, and provide pooling, memory, association, and attention mechanisms. We demonstrate the broad applicability of the Hopfield layers
across various domains. Hopfield layers improved state-of-the-art on three out of four considered multiple instance learning problems as well as on immune repertoire classification with several hundreds of thousands of instances. On the UCI benchmark collections of small classification tasks, where deep learning methods typically struggle, Hopfield layers yielded a new state-of-the-art when compared to different machine learning methods. Finally, Hopfield layers achieved state-of-the-art on two drug design datasets. The implementation is available at: \url{https://github.com/ml-jku/hopfield-layers}
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A unifying view on implicit bias in training linear neural networks
implicit bias implicit regularization convergence gradient flow gradient descent
We study the implicit bias of gradient flow (i.e., gradient descent with infinitesimal step size) on linear neural network training. We propose a tensor formulation of neural networks that includes fully-connected, diagonal, and convolutional networks as special cases, and investigate the linear version of the formulation called linear tensor networks. With this formulation, we can characterize the convergence direction of the network parameters as singular vectors of a tensor defined by the network. For $L$-layer linear tensor networks that are orthogonally decomposable, we show that gradient flow on separable classification finds a stationary point of the $\ell_{2/L}$ max-margin problem in a "transformed" input space defined by the network. For underdetermined regression, we prove that gradient flow finds a global minimum which minimizes a norm-like function that interpolates between weighted $\ell_1$ and $\ell_2$ norms in the transformed input space. Our theorems subsume existing results in the literature while removing standard convergence assumptions. We also provide experiments that corroborate our analysis.
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On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections
graphs fairness representation learning
This blog post discusses the ICLR 2021 paper "On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections" by Li et al., highlighting the importance of its theoretical results while critiquing the notions and applications of dyadic fairness provided. This paper presents a beautiful proof that can be followed to analyze representation disparities produced by various message-passing algorithms, and an algorithmic skeleton for improving the fairness of many message-passing algorithms. At the same time, it is essential that, as a community, we critically analyze for which applications a fair link prediction algorithm can distribute justice and contextualize our understandings of the politics and limitations of different notions of fairness in link prediction applications.
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Scaling Transformers for Skillful and Reliable Medium-range Weather Forecasting
weather forecasting climate modeling transformers deep learning
Weather forecasting is a fundamental problem for anticipating and mitigating the impacts of climate change. Recently, data-driven approaches for weather forecasting based on deep learning have shown great promise, achieving accuracies that are competitive with operational systems. However, those methods often employ complex, customized architectures without sufficient ablation analysis, making it difficult to understand what truly contributes to their success. Here we introduce Stormer, a simple transformer model that achieves state-of-the-art performance on weather forecasting with minimal changes to the standard transformer backbone. We identify the key components of Stormer through careful empirical analyses, including weather-specific embedding, randomized dynamics forecast, and pressure-weighted loss. At the core of Stormer is a randomized forecasting objective that trains the model to forecast the weather dynamics over varying time intervals. During inference, this allows us to produce multiple forecasts for a target lead time and combine them to obtain better forecast accuracy. On WeatherBench 2, Stormer performs competitively at short to medium-range forecasts and outperforms current methods beyond 7 days, while requiring orders-of-magnitude less training data and compute. Additionally, we demonstrate Stormer’s favorable scaling properties, showing consistent improvements in forecast accuracy with increases in model size and training tokens.
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Multi-Agent Imitation Learning with Copulas
multi-agent imitation learning dependence modeling copula
Multi-agent imitation learning aims to train multiple agents to perform tasks from demonstrations by learning a mapping between observations and actions, which is essential for understanding physical, social, and team-play systems. However, most existing works on modeling multi-agent interactions typically assume that agents make independent decisions based on their observations, ignoring the complex dependence among agents. In this paper, we propose to use copula, a powerful statistical tool for capturing dependence among random variables, to explicitly model the correlation and coordination in multi-agent systems. Our proposed model is able to separately learn marginals that capture the local behavioral patterns of each individual agent, as well as a copula function that solely and fully captures the dependence structure among agents. Extensive experiments on synthetic and real-world datasets show that our model outperforms state-of-the-art baselines across various scenarios in the action prediction task, and is able to generate new trajectories close to expert demonstrations.
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The token parser and manipulator, next-generation Deep Learning architecture
object-centric symbolic hybrid
Deep Learning is an excellently scalable approach for processing unstructured, high-dimensional, raw sensory signal. It is so good that these properties also becomes its most popular criticism. At the moment, deep learning is mostly just a giant correlation machine, devouring enormous amount of data to recognise hidden pattern in data, but still lacking in human-like systematic generalisation required in many reasoning tasks. Symbolic AI on the other hand possesses these abilities by design, but relied on handcrafted symbols that has already been abstracted away from the raw information. Among many approach to combine the best of both worlds, I am most excited about the end-to-end trainable architecture with a perception module that structurised the raw input and a reasoning module operates on top of these symbol-like vectors. While there are still a lot of work before such a system becomes practically relevant, in this blog post we will take a look at the paper Contrastive Learning of Structured World Model, an early paper that offer a glimpse into such architecture through a concrete implementation.
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Robust Curriculum Learning: from clean label detection to noisy label self-correction
curriculum learning noisy label robust learning training dynamics neural networks
Neural network training can easily overfit noisy labels resulting in poor generalization performance. Existing methods address this problem by (1) filtering out the noisy data and only using the clean data for training or (2) relabeling the noisy data by the model during training or by another model trained only on a clean dataset. However, the former does not leverage the features' information of wrongly-labeled data, while the latter may produce wrong pseudo-labels for some data and introduce extra noises. In this paper, we propose a smooth transition and interplay between these two strategies as a curriculum that selects training samples dynamically. In particular, we start with learning from clean data and then gradually move to learn noisy-labeled data with pseudo labels produced by a time-ensemble of the model and data augmentations. Instead of using the instantaneous loss computed at the current step, our data selection is based on the dynamics of both the loss and output consistency for each sample across historical steps and different data augmentations, resulting in more precise detection of both clean labels and correct pseudo labels. On multiple benchmarks of noisy labels, we show that our curriculum learning strategy can significantly improve the test accuracy without any auxiliary model or extra clean data.
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PolarNet: Learning to Optimize Polar Keypoints for Keypoint Based Object Detection
Object Detection Deep Learning
A variety of anchor-free object detectors have been actively proposed as possible alternatives to the mainstream anchor-based detectors that often rely on complicated design of anchor boxes. Despite achieving promising performance on par with anchor-based detectors, the existing anchor-free detectors such as FCOS or CenterNet predict objects based on standard Cartesian coordinates, which often yield poor quality keypoints. Further, the feature representation is also scale-sensitive. In this paper, we propose a new anchor-free keypoint based detector ``PolarNet", where keypoints are represented as a set of Polar coordinates instead of Cartesian coordinates. The ``PolarNet" detector learns offsets pointing to the corners of objects in order to learn high quality keypoints. Additionally, PolarNet uses features of corner points to localize objects, making the localization scale-insensitive. Finally in our experiments, we show that PolarNet, an anchor-free detector, outperforms the existing anchor-free detectors, and it is able to achieve highly competitive result on COCO test-dev benchmark ($47.8\%$ and $50.3\%$ AP under the single-model single-scale and multi-scale testing) which is on par with the state-of-the-art two-stage anchor-based object detectors. The code and the models are available at https://github.com/XiongweiWu/PolarNetV1
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Varying meaning complexity to explain and measure compositionality
compositionality systematicity task complexity
Compositionality is assumed to be a key property of language, but it is hard to observe in language emergence simulations. Following De Beule & Bergen (2006), we posit that the meaning of the datapoints that agents discuss must vary in complexity. We extend their work in different directions. First, we argue that this variation in the task is realistic and underlies the emergence of intersective adjectives and argument structure. Secondly, we show promising results for this hypothesis with attention-based neural networks. Thirdly, we argue that languages learned on tasks where meaning complexity varies are easier to analyse, and propose an intuitive metric called concatenability to illustrate this claim.
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InstantEmbedding: Efficient Local Node Representations
Node Embedding Structural Graph Representations Graph Embedding Local Algorithms
In this paper, we introduce InstantEmbedding, an efficient method for generating single-node representations using local PageRank computations. We prove that our approach produces globally consistent representations in sublinear time. We demonstrate this empirically by conducting extensive experiments on real-world datasets with over a billion edges. Our experiments confirm that InstantEmbedding requires drastically less computation time (over 9,000 times faster) and less memory (by over 8,000 times) to produce a single node’s embedding than traditional methods including DeepWalk, node2vec, VERSE, and FastRP. We also show that our method produces high quality representations, demonstrating results that meet or exceed the state of the art for unsupervised representation learning on tasks like node classification and link prediction.
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Inkorrect: Digital Ink Spelling Correction
digital ink correction inkorrect correction inkorrect online handwriting metrics capture quality new previous work
We introduce Inkorrect, a digital ink (online handwriting) spelling correction approach. We show that existing metrics don’t capture the quality of spelling correction, and propose a new one. Our approach outperforms previous work in automated and human evaluation, while also being more data- and label-efficient.
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Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification
transfer learning adversarial training influence functions limited data
Transfer learning has emerged as a powerful methodology for adapting pre-trained deep neural networks on image recognition tasks to new domains. This process consists of taking a neural network pre-trained on a large feature-rich source dataset, freezing the early layers that encode essential generic image properties, and then fine-tuning the last few layers in order to capture specific information related to the target situation. This approach is particularly useful when only limited or weakly labeled data are available for the new task. In this work, we demonstrate that adversarially-trained models transfer better than non-adversarially-trained models, especially if only limited data are available for the new domain task. Further, we observe that adversarial training biases the learnt representations to retaining shapes, as opposed to textures, which impacts the transferability of the source models. Finally, through the lens of influence functions, we discover that transferred adversarially-trained models contain more human-identifiable semantic information, which explains -- at least partly -- why adversarially-trained models transfer better.
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CANVASEMB: Learning Layout Representation with Large-scale Pre-training for Graphic Design
Layout Representation Pre-training
Layout representation, which models visual elements in a canvas and their inter-relations, plays a crucial role in graphic design intelligence.
With a large variety of layout designs and the unique characteristic of layouts that visual elements are defined as a list of categorical (e.g. shape type) and numerical (e.g. position and size) properties, it is challenging to learn a general and compact representation with limited data. Inspired by the recent success of self-supervised pre-training techniques in various natural language processing tasks, in this paper, we propose CanvasEmb (Canvas Embedding), which pre-trains deep representation from unlabeled graphic designs by jointly conditioning on all the context elements in the same canvas, with a multi-dimensional feature encoder and a multi-task learning objective. The pre-trained CanvasEmb model can be fine-tuned with just one additional output layer and with a small size of training data to create models for a wide range of downstream tasks. We verify our approach with presentation slides data. We construct a large-scale dataset with more than one million slides, and propose two novel layout understanding tasks with human labeling sets, namely element role labeling and image captioning. Evaluation results on these two tasks show that our model with fine-tuning achieves state-of-the-art performances. Furthermore, we conduct a deep analysis aiming to understand the modeling mechanism of CanvasEmb, and demonstrate its great potential use on more applications such as layout auto completion and layout retrieval.
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Run Away From your Teacher: a New Self-Supervised Approach Solving the Puzzle of BYOL
representation learning self-supervised learning contrastive learning regularization theory
Recently, a newly proposed self-supervised framework Bootstrap Your Own Latent (BYOL) seriously challenges the necessity of negative samples in contrastive-based learning frameworks. BYOL works like a charm despite the fact that it discards the negative samples completely and there is no measure to prevent collapse in its training objective. In this paper, we suggest understanding BYOL from the view of our newly proposed interpretable self-supervised learning framework, Run Away From your Teacher (RAFT). RAFT optimizes two objectives at the same time: (i) aligning two views of the same data to similar representations and (ii) running away from the model's Mean Teacher (MT, the exponential moving average of the history models) instead of BYOL's running towards it. The second term of RAFT explicitly prevents the representation collapse and thus makes RAFT a more conceptually reliable framework. We provide basic benchmarks of RAFT on CIFAR10 to validate the effectiveness of our method. Furthermore, we prove that BYOL is equivalent to RAFT under certain conditions, providing solid reasoning for BYOL's counter-intuitive success.
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GenQu: A Hybrid System for Learning Classical Data in Quantum States
Quantum Machine Learning Qubits Kernel Methods Deep Neural Network
Deep neural network-powered artificial intelligence has rapidly changed our daily life with various applications. However, as one of the essential steps of deep neural networks, training a heavily-weighted network requires a tremendous amount of computing resources. Especially in the post Moore's Law era, the limit of semiconductor fabrication technology has restricted the development of learning algorithms to cope with the increasing high-intensity training data. Meanwhile, quantum computing has exhibited its significant potential in terms of speeding up the traditionally compute-intensive workloads. For example, Google illustrates quantum supremacy by completing a sampling calculation task in 200 seconds, which is otherwise impracticable on the world's largest supercomputers. To this end, quantum-based learning becomes an area of interest, with the promising of a quantum speedup. In this paper, we propose GenQu, a hybrid and general-purpose quantum framework for learning classical data through quantum states. We evaluate GenQu with real datasets and conduct experiments on both simulations and real quantum computer IBM-Q. Our evaluation demonstrates that, comparing with classical solutions, the proposed models running on GenQu framework achieve similar accuracy with a much smaller number of qubits, while significantly reducing the parameter size by up to 95.8\% and converging speedup by 66.67% faster.
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Moving Beyond Navigation with Active Neural SLAM
navigation reinforcement learning domain generalization
The ability to effectively harness autonomous control in real-world 3D environments largely depends on learning realistic navigation techniques for embodied agents. Advances to classical robotics call for efficient methods to navigate, viz., explore unseen simulations while transferring the attained performance to the real-world domain. Any autonomous agent in the wild should have the ability to make decisions on the fly, having explored its surroundings and being cognizant of sudden changes, as in the real world. This blog post breaks down the method of Active Neural SLAM for exploring 3D environments, moving away from end-to-end exploration, by learning policies in a modular and hierarchical manner. Active Neural SLAM enabled Domain Generalization to previously unseen environments and Task Generalization in navigation while curbing the high sample complexity of previous methods. We also suggest ways to leverage this method for scene understanding and learning agent-scene interaction by combining navigation with motion prediction or synthesis.
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Estimating Field Parameters from Multiphysics Governing Equations with Scarce Data
Parameter estimation Differential equations Machine learning
Real-world physical phenomena often involve complex, coupled behaviors influenced by spatially distributed physical properties. This complexity poses significant challenges for modeling, particularly when faced with limited or noisy observations. In this work, we introduce NeuroPIPE for field parameters inference in partial differential equations (PDEs). We employ deep neural networks to model the field parameters while solving the PDEs in discretized form using the finite difference method (FDM). Focusing on a representative example of cardiac electrophysiology with just a few hundred measurements, NeuroPIPE accurately captures the underlying parameters and physical behaviors. We demonstrated that our approach surpasses the state-of-the-art physics-informed neural networks (PINN) in terms of model robustness, parameter estimation accuracy, and training efficiency. Even with abundant training data, PINN fails in parameter inference in some cases, whereas NeuroPIPE consistently performs well. Additionally, NeuroPIPE achieves significantly higher inference accuracy by one order of magnitude. Our approach holds substantial promise for learning and understanding many complex physics problems with a significant reduction of training data.
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Why Convolutional Networks Learn Oriented Bandpass Filters: Theory and Empirical Support
bandpass filters convolutional architectures filters structure convolutional networks empirical support eigenfunctions theory images filter characteristics
It has been repeatedly observed that convolutional architectures when applied to image understanding tasks learn oriented bandpass filters. A standard explanation of this result is that these filters reflect the structure of the images that they have been exposed to during training: Natural images typically are locally composed of oriented contours at various scales and oriented bandpass filters are matched to such structure. We offer an alternative explanation based not on the structure of images, but rather on the structure of convolutional architectures. In particular, complex exponentials are the eigenfunctions of convolution. These eigenfunctions are defined globally; however, convolutional architectures operate locally. To enforce locality, one can apply a windowing function to the eigenfunctions, which leads to oriented bandpass filters as the natural operators to be learned with convolutional architectures. From a representational point of view, these filters allow for a local systematic way to characterize and operate on an image or other signal. We offer empirical support for the hypothesis that convolutional networks learn such filters at all of their convolutional layers. While previous research has shown evidence of filters having oriented bandpass characteristics at early layers, ours appears to be the first study to document the predominance of such filter characteristics at all layers. Previous studies have missed this observation because they have concentrated on the cumulative compositional effects of filtering across layers, while we examine the filter characteristics that are present at each layer.
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Semantic Image Synthesis with Semantically Coupled VQ-Model
semantic image synthesis vector quantization autoregressive generative model
Semantic image synthesis enables control over unconditional image generation by allowing guidance on what is being generated. We conditionally synthesize the latent space from a vector quantized model (VQ-model) pre-trained to autoencode images. Instead of training an autoregressive Transformer on separately learned conditioning latents and image latents, we find that jointly learning the conditioning and image latents significantly improves the modeling capabilities of the Transformer model. While our jointly trained VQ-model achieves a similar reconstruction performance to a vanilla VQ-model for both semantic and image latents, tying the two modalities at the autoencoding stage proves to be an important ingredient to improve autoregressive modeling performance. We show that our model improves semantic image synthesis using autoregressive models on popular semantic image datasets ADE20k, Cityscapes and COCO-Stuff.
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DCT-SNN: Using DCT to Distribute Spatial Information over Time for Learning Low-Latency Spiking Neural Networks
Spiking Neural Networks Input Encoding Low Latency Discrete Cosine Transform Temporal Information Frequency Domain
Spiking Neural Networks (SNNs) offer a promising alternative to traditional deep learning frameworks, since they provide higher
computational efficiency due to event-driven information processing. SNNs distribute the analog values of pixel intensities into binary spikes over time. However, the most widely used input coding schemes, such as Poisson based rate-coding, do not leverage the additional temporal learning capability of SNNs effectively. Moreover, these SNNs suffer from high inference latency which is a major bottleneck to their deployment. To overcome this, we propose a scalable time-based encoding scheme that utilizes the Discrete Cosine Transform (DCT) to reduce the number of timesteps required for inference. DCT decomposes an image into a weighted sum of sinusoidal basis images. At each time step, a single frequency base, taken in order and modulated
by its corresponding DCT coefficient, is input to an accumulator that generates spikes upon crossing a threshold. We use the proposed scheme to learn DCT-SNN, a low-latency deep SNN with leaky-integrate-and-fire neurons, trained using surrogate gradient descent based backpropagation. We achieve top-1 accuracy of 89.94%, 68.3% and 52.43% on CIFAR-10, CIFAR-100 and TinyImageNet, respectively using VGG architectures. Notably, DCT-SNN performs inference with 2-14X reduced latency compared to other state-of-the-art SNNs, while achieving comparable accuracy to their standard deep learning counterparts. The dimension of the transform allows us to control the number of timesteps required for inference. Additionally, we can trade-off accuracy with latency in a principled manner by dropping the highest frequency components during inference.
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Nonseparable Symplectic Neural Networks
Data-driven modeling nonseparable Hailtonian system symplectic networks
Predicting the behaviors of Hamiltonian systems has been drawing increasing attention in scientific machine learning. However, the vast majority of the literature was focused on predicting separable Hamiltonian systems with their kinematic and potential energy terms being explicitly decoupled, while building data-driven paradigms to predict nonseparable Hamiltonian systems that are ubiquitous in fluid dynamics and quantum mechanics were rarely explored. The main computational challenge lies in the effective embedding of symplectic priors to describe the inherently coupled evolution of position and momentum, which typically exhibits intricate dynamics. To solve the problem, we propose a novel neural network architecture, Nonseparable Symplectic Neural Networks (NSSNNs), to uncover and embed the symplectic structure of a nonseparable Hamiltonian system from limited observation data. The enabling mechanics of our approach is an augmented symplectic time integrator to decouple the position and momentum energy terms and facilitate their evolution. We demonstrated the efficacy and versatility of our method by predicting a wide range of Hamiltonian systems, both separable and nonseparable, including chaotic vortical flows. We showed the unique computational merits of our approach to yield long-term, accurate, and robust predictions for large-scale Hamiltonian systems by rigorously enforcing symplectomorphism.
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Unsupervised Discovery of Interpretable Latent Manipulations in Language VAEs
interpretability unsupervised interpretable directions controllable text generation
Language generation models are attracting more and more attention due to their constantly increasing quality and remarkable generation results. State-of-the-art NLG models like BART/T5/GPT-3 do not have latent spaces, therefore there is no natural way to perform controlled generation. In contrast, less popular models with explicit latent spaces have the innate ability to manipulate text attributes by moving along latent directions. For images, properties of latent spaces are well-studied: there exist interpretable directions (e.g. zooming, aging, background removal) and they can even be found without supervision. This success is expected: latent space image models, especially GANs, achieve state-of-the-art generation results and hence have been the focus of the research community. For language, this is not the case: text GANs are hard to train because of non-differentiable discrete data generation, and language VAEs suffer from posterior collapse and fill the latent space poorly. This makes finding interpetable text controls challenging. In this work, we make the first step towards unsupervised discovery of interpretable directions in language latent spaces. For this, we turn to methods shown to work in the image domain. Surprisingly, we find that running PCA on VAE representations of training data consistently outperforms shifts along the coordinate and random directions. This approach is simple, data-adaptive, does not require training and discovers meaningful directions, e.g. sentence length, subject age, and verb tense. Our work lays foundations for two important areas: first, it allows to compare models in terms of latent space interpretability, and second, it provides a baseline for unsupervised latent controls discovery.
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Rethinking ValueDice: Does It Really Improve Performance?
reinforcement learning imitation learning ValueDice adversarial learning
Since the introduction of GAIL, adversarial imitation learning (AIL) methods attract lots of research interests. Among these methods, ValueDice has achieved significant improvements: it beats the classical approach Behavioral Cloning (BC) in the offline setting, and it requires fewer interactions than GAIL in the online setting. Are these improvements benefited from more advanced algorithm designs? We answer this question with the following conclusions.
First, we show that the ValueDice could reduce to BC in the offline setting. Second, we verify that overfitting exists and regularization matters. Specifically, we demonstrate that with weight decay, BC also nearly matches the expert performance as ValueDice does. The first two claims explain the superior offline performance of ValueDice. Third, we establish that ValueDice does not work at all when the expert trajectory is subsampled. Instead, the mentioned success holds when the expert trajectory is complete, in which ValueDice is closely related to BC that performs well as mentioned. Finally, we discuss the implications of our research for imitation learning studies beyond ValueDice.
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On the Power of Abstention and Data-Driven Decision Making for Adversarial Robustness
Adversarial Machine Learning Learning Theory
We formally define a feature-space attack where the adversary can perturb datapoints by arbitrary amounts but in restricted directions. By restricting the attack to a small random subspace, our model provides a clean abstraction for non-Lipschitz networks which map small input movements to large feature movements. We prove that classifiers with the ability to abstain are provably more powerful than those that cannot in this setting. Specifically, we show that no matter how well-behaved the natural data is, any classifier that cannot abstain will be defeated by such an adversary. However, by allowing abstention, we give a parameterized algorithm with provably good performance against such an adversary when classes are reasonably well-separated in feature space and the dimension of the feature space is high. We further use a data-driven method to set our algorithm parameters to optimize over the accuracy vs. abstention trade-off with strong theoretical guarantees. Our theory has direct applications to the technique of contrastive learning, where we empirically demonstrate the ability of our algorithms to obtain high robust accuracy with only small amounts of abstention in both supervised and self-supervised settings. Our results provide a first formal abstention-based gap, and a first provable optimization for the induced trade-off in an adversarial defense setting.
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Data-efficient Hindsight Off-policy Option Learning
Hierarchical Reinforcement Learning Off-Policy Abstractions Data-Efficiency
Hierarchical approaches for reinforcement learning aim to improve data efficiency and accelerate learning by incorporating different abstractions. We introduce Hindsight Off-policy Options (HO2), an efficient off-policy option learning algorithm, and isolate the impact of action and temporal abstraction in the option framework by comparing flat policies, mixture policies without temporal abstraction, and finally option policies; all with comparable policy optimization. When aiming for data efficiency, we demonstrate the importance of off-policy optimization, as even flat policies trained off-policy can outperform on-policy option methods. In addition, off-policy training and backpropagation through a dynamic programming inference procedure -- through time and through the policy components for every time-step -- enable us to train all components' parameters independently of the data-generating behavior policy. We continue to illustrate challenges in off-policy option learning and the related importance of trust-region constraints. Experimentally, we demonstrate that HO2 outperforms existing option learning methods and that both action and temporal abstraction provide strong benefits in particular in more demanding simulated robot manipulation tasks from raw pixel inputs. Finally, we develop an intuitive extension to encourage temporal abstraction and investigate differences in its impact between learning from scratch and using pre-trained options.
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Decorrelated Double Q-learning
q-learning control variates reinforcement learning
Q-learning with value function approximation may have the poor performance because of overestimation bias and imprecise estimate. Specifically, overestimation bias is from the maximum operator over noise estimate, which is exaggerated using the estimate of a subsequent state. Inspired by the recent advance of deep reinforcement learning and Double Q-learning, we introduce the decorrelated double Q-learning (D2Q). Specifically, we introduce the decorrelated regularization item to reduce the correlation between value function approximators, which can lead to less biased estimation and low variance. The experimental results on a suite of MuJoCo continuous control tasks demonstrate that our decorrelated double Q-learning can effectively improve the performance.
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EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL
Offline Reinforcement Learning Off-Policy Reinforcement Learning
Off-policy reinforcement learning (RL) holds the promise of sample-efficient learning of decision-making policies by leveraging past experience. However, in the offline RL setting -- where a fixed collection of interactions are provided and no further interactions are allowed -- it has been shown that standard off-policy RL methods can significantly underperform. Recently proposed methods often aim to address this shortcoming by constraining learned policies to remain close to the given dataset of interactions. In this work, we closely investigate an important simplification of BCQ~\citep{fujimoto2018off} -- a prior approach for offline RL -- which removes a heuristic design choice and naturally restrict extracted policies to remain \emph{exactly} within the support of a given behavior policy. Importantly, in contrast to their original theoretical considerations, we derive this simplified algorithm through the introduction of a novel backup operator, Expected-Max Q-Learning (EMaQ), which is more closely related to the resulting practical algorithm. Specifically, in addition to the distribution support, EMaQ explicitly considers the number of samples and the proposal distribution, allowing us to derive new sub-optimality bounds which can serve as a novel measure of complexity for offline RL problems. In the offline RL setting -- the main focus of this work -- EMaQ matches and outperforms prior state-of-the-art in the D4RL benchmarks~\citep{fu2020d4rl}. In the online RL setting, we demonstrate that EMaQ is competitive with Soft Actor Critic (SAC). The key contributions of our empirical findings are demonstrating the importance of careful generative model design for estimating behavior policies, and an intuitive notion of complexity for offline RL problems. With its simple interpretation and fewer moving parts, such as no explicit function approximator representing the policy, EMaQ serves as a strong yet easy to implement baseline for future work.
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MSFM: Multi-Scale Fusion Module for Object Detection
Feature Fusion Object Detection Multi-Scale
Feature fusion is beneficial to object detection tasks in two folds. On one hand, detail and position information can be combined with semantic information when high and low-resolution features from shallow and deep layers are fused. On the other hand, objects can be detected in different scales, which improves the robustness of the framework. In this work, we present a Multi-Scale Fusion Module (MSFM) that extracts both detail and semantical information from a single input but at different scales within the same layer. Specifically, the input of the module will be resized into different scales on which position and semantic information will be processed, and then they will be rescaled back and combined with the module input. The MSFM is lightweight and can be used as a drop-in layer to many existing object detection frameworks. Experiments show that MSFM can bring +2.5% mAP improvement with only 2.4M extra parameters on Faster R-CNN with ResNet-50 FPN backbone on COCO Object Detection minival set, outperforming that with ResNet-101 FPN backbone without the module which obtains +2.0% mAP with 19.0M extra parameters. The best resulting model achieves a 45.7% mAP on test-dev set. Code will be available.
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Skip-connection and batch-normalization improve data separation ability
Deep learning ResNet Skip-connection Batch-normalization
The ResNet and the batch-normalization (BN) achieved high performance even when only a few labeled data are available. However, the reasons for its high performance are unclear. To clear the reasons, we analyzed the effect of the skip-connection in ResNet and the BN on the data separation ability, which is an important ability for the classification problem. Our results show that, in the multilayer perceptron with randomly initialized weights, the angle between two input vectors converges to zero in an exponential order of its depth, that the skip-connection makes this exponential decrease into a sub-exponential decrease, and that the BN relaxes this sub-exponential decrease into a reciprocal decrease. Moreover, our analysis shows that the preservation of the angle at initialization encourages trained neural networks to separate points from different classes. These imply that the skip-connection and the BN improve the data separation ability and achieve high performance even when only a few labeled data are available.
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Pseudo Label-Guided Multi Task Learning for Scene Understanding
Multi-task learning monocular depth estimation semantic segmentation pseudo label cross-view consistency
Multi-task learning (MTL) for scene understanding has been actively studied by exploiting correlation of multiple tasks. This work focuses on improving the performance of the MTL network that infers depth and semantic segmentation maps from a single image. Specifically, we propose a novel MTL architecture, called Pseudo-MTL, that introduces pseudo labels for joint learning of monocular depth estimation and semantic segmentation tasks. The pseudo ground truth depth maps, generated from pretrained stereo matching methods, are leveraged to supervise the monocular depth estimation. More importantly, the pseudo depth labels serve to impose a cross-view consistency on the estimated monocular depth and segmentation maps of two views. This enables for mitigating the mismatch problem incurred by inconsistent prediction results across two views. A thorough ablation study validates that the cross-view consistency leads to a substantial performance gain by ensuring inference-view invariance for the two tasks.
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Normalisation is dead, long live normalisation!
normalization initialization propagation skip connections residual networks
Since the advent of Batch Normalisation (BN) almost every state-of-the-art (SOTA) method uses some form of normalisation.
After all, normalisation generally speeds up learning and leads to models that generalise better than their unnormalised counterparts.
This turns out to be especially useful when using some form of skip connections, which are prominent in Residual Networks (ResNets), for example.
However, Brock et al. (2021a) suggest that SOTA performance can also be achieved using ResNets without normalisation!
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SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing
score context representation conversational semantic task structured ontology lms conversational semantic parsing csp sequence natural language queries
Conversational Semantic Parsing (CSP) is the task of converting a sequence of natural language queries to formal language (e.g., SQL, SPARQL) that can be executed against a structured ontology (e.g. databases, knowledge bases). To accomplish this task, a CSP system needs to model the relation between the unstructured language utterance and the structured ontology while representing the multi-turn dynamics of the dialog. Pre-trained language models (LMs) are the state-of-the-art for various natural language processing tasks. However, existing pre-trained LMs that use language modeling training objectives over free-form text have limited ability to represent natural language references to contextual structural data. In this work, we present SCORE, a new pre-training approach for CSP tasks designed to induce representations that capture the alignment between the dialogue flow and the structural context. We demonstrate the broad applicability of SCORE to CSP tasks by combining SCORE with strong base systems on four different tasks (SPARC, COSQL, MWOZ, and SQA). We show that SCORE can improve the performance over all these base systems by a significant margin and achieves state-of-the-art results on three of them.
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Shapley explainability on the data manifold
shapley explainability explainability data predictions model data manifold explainability crucial model development compliance
Explainability in AI is crucial for model development, compliance with regulation, and providing operational nuance to predictions. The Shapley framework for explainability attributes a model’s predictions to its input features in a mathematically principled and model-agnostic way. However, general implementations of Shapley explainability make an untenable assumption: that the model’s features are uncorrelated. In this work, we demonstrate unambiguous drawbacks of this assumption and develop two solutions to Shapley explainability that respect the data manifold. One solution, based on generative modelling, provides flexible access to data imputations; the other directly learns the Shapley value-function, providing performance and stability at the cost of flexibility. While “off-manifold” Shapley values can (i) give rise to incorrect explanations, (ii) hide implicit model dependence on sensitive attributes, and (iii) lead to unintelligible explanations in higher-dimensional data, on-manifold explainability overcomes these problems.
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A Probabilistic Model for Discriminative and Neuro-Symbolic Semi-Supervised Learning
semi-supervised learning probabilistic model neuro-symbolic learning
Strong progress has been achieved in semi-supervised learning (SSL) by combining several methods, some of which relate to properties of the data distribution p(x), others to the model outputs p(y|x), e.g. minimising the entropy of unlabelled predictions. Focusing on the latter, we fill a gap in the standard text by introducing a probabilistic model for discriminative semi-supervised learning, mirroring the classical generative model. Several SSL methods are theoretically explained by our model as inducing (approximate) strong priors over parameters of p(y|x). Applying this same probabilistic model to tasks in which labels represent binary attributes, we theoretically justify a family of neuro-symbolic SSL approaches, taking a step towards bridging the divide between statistical learning and logical reasoning.
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Transformers with Competitive Ensembles of Independent Mechanisms
transformer mechanism modularity modules independence
An important development in deep learning from the earliest MLPs has been a move towards architectures with structural inductive biases which enable the model to keep distinct sources of information and routes of processing well-separated. This structure is linked to the notion of independent mechanisms from the causality literature, in which a mechanism is able to retain the same processing as irrelevant aspects of the world are changed. For example, convnets enable separation over positions, while attention-based architectures (especially Transformers) learn which combination of positions to process dynamically. In this work we explore a way in which the Transformer architecture is deficient: it represents each position with a large monolithic hidden representation and a single set of parameters which are applied over the entire hidden representation. This potentially throws unrelated sources of information together, and limits the Transformer's ability to capture independent mechanisms. To address this, we propose Transformers with Independent Mechanisms (TIM), a new Transformer layer which divides the hidden representation and parameters into multiple mechanisms, which only exchange information through attention. Additionally, we propose a competition mechanism which encourages these mechanisms to specialize over time steps, and thus be more independent. We study TIM on a large scale BERT model, on the Image Transformer, and on speech enhancement and find evidence for semantically meaningful specialization as well as improved performance.
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RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs
Knowledge Graph Reasoning Logic Rules EM
This paper studies learning logic rules for reasoning on knowledge graphs. Logic rules provide interpretable explanations when used for prediction as well as being able to generalize to other tasks, and hence are critical to learn. Existing methods either suffer from the problem of searching in a large search space (e.g., neural logic programming) or ineffective optimization due to sparse rewards (e.g., techniques based on reinforcement learning). To address these limitations, this paper proposes a probabilistic model called RNNLogic. RNNLogic treats logic rules as a latent variable, and simultaneously trains a rule generator as well as a reasoning predictor with logic rules. We develop an EM-based algorithm for optimization. In each iteration, the reasoning predictor is updated to explore some generated logic rules for reasoning. Then in the E-step, we select a set of high-quality rules from all generated rules with both the rule generator and reasoning predictor via posterior inference; and in the M-step, the rule generator is updated with the rules selected in the E-step. Experiments on four datasets prove the effectiveness of RNNLogic.
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ZCal: Machine learning methods for calibrating radio interferometric data
Radio astronomy Calibration Radio interferometry ska kat-7 MeerKat
Calibration is the most critical data processing step needed for generating images of high dynamic range \citep{editioncasa}. With ever-increasing data volumes produced by modern radio telescopes \cite{aniyan2017classifying}, astronomers are overwhelmed by the amount of data that needs to be manually processed and analyzed using limited computational resources \citep{yatawatta2020stochastic}. Therefore, intelligent and automated systems are required to overcome these challenges. Traditionally, astronomers use a package such as Common Astronomy Software Applications (CASA) to compute the gain solutions based on regular observations of a known calibrator source \citep{thompson2017interferometry} \citep{abebe2015study} \citep{grobler2016calibration} \citep{editioncasa}. The traditional approach to calibration is iterative and time-consuming \citep{jajarmizadeh2017optimal}, thus, the proposal of machine learning techniques. The applications of machine learning have created an opportunity to deal with complex problems currently encountered in radio astronomy data processing \citep{aniyan2017classifying}. In this work, we propose the use of supervised machine learning models to first generation calibration (1GC), using the KAT-7 telescope environmental and pointing sensor data recorded during observations. Applying machine learning to 1GC, as opposed to calculating the gain solutions in CASA, has shown evidence of reducing computation, as well as accurately predicting the 1GC gain solutions and antenna behaviour. These methods are computationally less expensive, however they have not fully learned to generalise in predicting accurate 1GC solutions by looking at environmental and pointing sensors. We use an ensemble multi-output regression models based on random forest, decision trees, extremely randomized trees and K-nearest neighbor algorithms. The average prediction error obtained during the testing of our models on testing data is $ \approx 0.01 < rmse < 0.09$ for gain amplitude per antenna, and $0.2 rad < rmse <0.5 rad$ for gain phase. This shows that the instrumental parameters used to train our model strongly correlate with gain amplitude effects than a phase.
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HalentNet: Multimodal Trajectory Forecasting with Hallucinative Intents
halentnet multimodal trajectory forecasting essential model better intents votes hallucinative intents halentnet intelligent decisions
Motion forecasting is essential for making intelligent decisions in robotic navigation. As a result, the multi-agent behavioral prediction has become a core component of modern human-robot interaction applications such as autonomous driving. Due to various intentions and interactions among agents, agent trajectories can have multiple possible futures. Hence, the motion forecasting model's ability to cover possible modes becomes essential to enable accurate prediction. Towards this goal, we introduce HalentNet to better model the future motion distribution in addition to a traditional trajectory regression learning objective by incorporating generative augmentation losses. We model intents with unsupervised discrete random variables whose training is guided by a collaboration between two key signals: A discriminative loss that encourages intents' diversity and a hallucinative loss that explores intent transitions (i.e., mixed intents) and encourages their smoothness. This regulates the neural network behavior to be more accurately predictive on uncertain scenarios due to the active yet careful exploration of possible future agent behavior. Our model's learned representation leads to better and more semantically meaningful coverage of the trajectory distribution. Our experiments show that our method can improve over the state-of-the-art trajectory forecasting benchmarks, including vehicles and pedestrians, for about 20% on average FDE and 50% on road boundary violation rate when predicting 6 seconds future. We also conducted human experiments to show that our predicted trajectories received 39.6% more votes than the runner-up approach and 32.2% more votes than our variant without hallucinative mixed intent loss. The code will be released soon.
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Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations
Explanations Concept based explanations Learning symbolic representations Sequential Decision Making Planning Reinforcement learning.
As increasingly complex AI systems are introduced into our daily lives, it becomes important for such systems to be capable of explaining the rationale for their decisions and allowing users to contest these decisions. A significant hurdle to allowing for such explanatory dialogue could be the vocabulary mismatch between the user and the AI system. This paper introduces methods for providing contrastive explanations in terms of user-specified concepts for sequential decision-making settings where the system's model of the task may be best represented as a blackbox simulator. We do this by building partial symbolic models of a local approximation of the task that can be leveraged to answer the user queries. We empirically test these methods on a popular Atari game (Montezuma's Revenge) and modified versions of Sokoban (a well-known planning benchmark) and report the results of user studies to evaluate whether people find explanations generated in this form useful.
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A Real-time Contribution Measurement Method for Participants in Federated Learning
Federated Learning Contribution Evaluation Multi-party Participation
Federated learning is a framework for protecting distributed data privacy and has participated in commercial activities. However, there is a lack of a sufficiently reasonable contribution measurement mechanism to distribute the reward for each agent. In the commercial union, if there is no mechanism like this, every agent will get the same reward. This is unfair to agents that provide better data, so such a mechanism is needed. To address this issue, this work proposes a real-time contribution measurement method. Firstly, the method defines the impact of each agent. Furthermore, we comprehensively consider the current round and the previous round to obtain the contribution rate of each agent. To verify effectiveness of the proposed method, the work conducts pseudo-distributed training and an experiment on the Penn Treebank dataset. Comparing the Shapley Value in game theory, the comparative experiment result shows that the proposed method is more sensitive to both data quantity and data quality under the premise of maintaining real-time.
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Rotograd: Dynamic Gradient Homogenization for Multitask Learning
multitask learning deep learning gradnorm
GradNorm (Chen et al., 2018) is a broadly used gradient-based approach for training multitask networks, where different tasks share, and thus compete during learning, for the network parameters. GradNorm eases the fitting of all individual tasks by dynamically equalizing the contribution of each task to the overall gradient magnitude. However, it does not prevent the individual tasks’ gradients from conflicting, i.e., pointing towards opposite directions, and thus resulting in a poor multitask performance. In this work we propose Rotograd, an extension to GradNorm that addresses this problem by dynamically homogenizing not only the gradient magnitudes but also their directions across tasks. For this purpose,Rotograd adds a layer of task-specific rotation matrices that aligns all the task gradients. Importantly, we then analyze Rotograd (and its predecessor) through the lens of game theory, providing theoretical guarantees on the algorithm stability and convergence. Finally, our experiments on several real-world datasets and network architectures show that Rotograd outperforms previous approaches for multitask learning.
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Black-Box Optimization Revisited: Improving Algorithm Selection Wizards through Massive Benchmarking
black-box optimization mujoco wizard benchmarking BBOB LSGO
Existing studies in black-box optimization for machine learning suffer from low
generalizability, caused by a typically selective choice of problem instances used
for training and testing different optimization algorithms. Among other issues,
this practice promotes overfitting and poor-performing user guidelines. To address
this shortcoming, we propose in this work a benchmark suite, OptimSuite,
which covers a broad range of black-box optimization problems, ranging from
academic benchmarks to real-world applications, from discrete over numerical
to mixed-integer problems, from small to very large-scale problems, from noisy
over dynamic to static problems, etc. We demonstrate the advantages of such a
broad collection by deriving from it Automated Black Box Optimizer (ABBO), a
general-purpose algorithm selection wizard. Using three different types of algorithm
selection techniques, ABBO achieves competitive performance on all
benchmark suites. It significantly outperforms previous state of the art on some of
them, including YABBOB and LSGO. ABBO relies on many high-quality base
components. Its excellent performance is obtained without any task-specific
parametrization. The benchmark collection, the ABBO wizard, its base solvers,
as well as all experimental data are reproducible and open source in OptimSuite.
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Real-Time Anomaly Detection for Multivariate Data Stream
machine-learning signal-processing
The paper titled “Probabilistic reasoning for streaming anomaly detection” from MIT CSAIL proposed a framework for performing online anomaly detection on univariate data. Unfortunately, most of the data in the real world are multivariate. Hence, mandating the need for more research into performing online anomaly detection in multivariate data. We have been inspired by their work and extended their framework to support multivariate data with some clever optimizations to build a scalable system.
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