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GraphCGAN: Convolutional Graph Neural Network with Generative Adversarial Networks
graphcgan generative adversarial networks gcn ssl gan performance convolutional networks superior performances
Graph convolutional networks (GCN) achieved superior performances in graph-based semi-supervised learning (SSL) tasks.
Generative adversarial networks (GAN) also show the ability to increase the performance in SSL.
However, there is still no good way to combine the GAN and GCN in graph-based SSL tasks.
In this work, we present GraphCGAN, a novel framework to incorporate adversarial learning with convolution-based graph neural networks, to operate on graph-structured data.
In GraphCGAN, we show that generator can generate topology structure and features of fake nodes jointly and boost the performance of convolution-based graph neural networks classifier.
In a number of experiments on benchmark datasets, we show that the proposed GraphCGAN outperforms the baseline methods by a significant margin.
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Opportunities and Challenges of Frontier Data Governance With Synthetic Data
Synthetic Data Data AI Governance Accountability Trust Regulation Data Governance Bias Alignment
Synthetic data, or data generated by machine learning models, is increasingly emerging as a solution to the data access problem. However, its use introduces significant governance and accountability challenges, and potentially debases existing governance paradigms, such as compute and data governance. In this paper, we identify 3 key governance and accountability challenges that synthetic data poses - it can enable the increased emergence of malicious actors, spontaneous biases and value drift. We thus craft 3 technical mechanisms to address these specific challenges, finding applications for synthetic data towards adversarial training, bias mitigation and value reinforcement. These could not only counteract the risks of synthetic data, but serve as critical levers for governance of the frontier in the future.
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PDE-regularized Neural Networks for Image Classification
Neural ODE Partial Differential Equations Image Classification
Neural ordinary differential equations (neural ODEs) introduced an approach to approximate a neural network as a system of ODEs after considering its layer as a continuous variable and discretizing its hidden dimension. While having several good characteristics, neural ODEs are known to be numerically unstable and slow in solving their integral problems, resulting in errors and/or much computation of the forward-pass inference. In this work, we present a novel partial differential equation (PDE)-based approach that removes the necessity of solving integral problems and considers both the layer and the hidden dimension as continuous variables. Owing to the recent advancement of learning PDEs, the presented novel concept, called PR-Net, can be implemented. Our method shows comparable (or better) accuracy and robustness in much shorter forward-pass inference time for various datasets and tasks in comparison with neural ODEs and Isometric MobileNet V3. For the efficient nature of PR-Net, it is suitable to be deployed in resource-scarce environments, e.g., deploying instead of MobileNet.
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Exchanging Lessons Between Algorithmic Fairness and Domain Generalization
algorithmic fairness domain generalization representation learning invariance
Standard learning approaches are designed to perform well on average for the data distribution available at training time. Developing learning approaches that are not overly sensitive to the training distribution is central to research on domain- or out-of-distribution generalization, robust optimization and fairness. In this work we focus on links between research on domain generalization and algorithmic fairness---where performance under a distinct but related test distributions is studied---and show how the two fields can be mutually beneficial. While domain generalization methods typically rely on knowledge of disjoint "domains" or "environments", "sensitive" label information indicating which demographic groups are at risk of discrimination is often used in the fairness literature. Drawing inspiration from recent fairness approaches that improve worst-case performance without knowledge of sensitive groups, we propose a novel domain generalization method that handles the more realistic scenario where environment partitions are not provided. We then show theoretically and empirically how different partitioning schemes can lead to increased or decreased generalization performance, enabling us to outperform Invariant Risk Minimization with handcrafted environments in multiple cases. We also show how a re-interpretation of IRMv1 allows us for the first time to directly optimize a common fairness criterion, group-sufficiency, and thereby improve performance on a fair prediction task.
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Generative PDE Control
PDE control physical simulation generative models prior reweighting
Controlling PDE is a fundamental task across science and engineering. Classical techniques for PDE control tend to be computationally demanding and recent deep learning-based approaches often struggle to optimize long-term control sequences. In this work, we introduce Diffusion generative PDE Control (DiffConPDE), a new class of method to address the PDE control problem. DiffConPDE excels by simultaneously minimizing both the learned generative energy function and the predefined control objectives across the entire trajectory and control sequence. Moreover, we enhance DiffConPDE with prior reweighting, enabling the discovery of control sequences that significantly deviate from the training distribution. We test our method in 2D jellyfish movement in a fluid environment and 1D Burgers' equation control. Our method consistently outperforms baselines. Notably, DiffConPDE unveils an intriguing fast-close-slow-open pattern observed in the jellyfish, aligning with established findings in the field of fluid dynamics.
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Order Independence With Finetuning
large language models order dependence trust fairness finetuning multiple choice questions
Large language models (LLMs) demonstrate remarkable performance on many NLP tasks, yet often exhibit order dependence: simply reordering semantically identical tokens (e.g., answer choices in multiple-choice questions) can lead to inconsistent predictions. Recent work proposes Set-Based Prompting (SBP) as a way to remove order information from designated token subsets, thereby mitigating positional biases. However, applying SBP on base models induces an out-of-distribution input format, which can degrade in-distribution performance. We introduce a fine-tuning strategy that integrates SBP into the training process, “pulling” these set-formatted prompts closer to the model’s training manifold. We show that SBP can be incorporated into a model via fine-tuning. Our experiments on in-distribution (MMLU) and out-of-distribution (CSQA, ARC Challenge) multiple-choice tasks show that SBP fine-tuning significantly improves accuracy and robustness to answer-order permutations, all while preserving broader language modeling capabilities. We discuss the broader implications of order-invariant modeling and outline future directions for building fairer, more consistent LLMs.
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Evaluation of Feature-based explanations
Explainableai evaluation
This blog post describes contribution of the paper titled "EVALUATIONS AND METHODS FOR EXPLANATION THROUGH ROBUSTNESS ANALYSIS" by [Cheng et. al](https://openreview.net/forum?id=Hye4KeSYDr) that discusses assessing robustness in a novel way and coming up with more robust explanation in the specific area of insertion and removal of explanations. This is because such explanations face two drawbacks:
1. When the feature importance is estimated by removing a feature by setting it to a baseline value, it has higher chance to attribute high importance if some values deflect a lot from baseline. An example would be setting RGB pixels to black, that will give bright pixels more importance.
2. When the feature importance is estimated by removing a feature by giving it some value sampled from the distribution (using a generative model) , there is an inherent bias that goes from the generative model to this process and not all domains can have a proper generative models.
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Trace norm regularization and faster inference for embedded speech recognition RNNs
LVCSR speech recognition embedded low rank factorization RNN GRU trace norm
We propose and evaluate new techniques for compressing and speeding up dense matrix multiplications as found in the fully connected and recurrent layers of neural networks for embedded large vocabulary continuous speech recognition (LVCSR). For compression, we introduce and study a trace norm regularization technique for training low rank factored versions of matrix multiplications. Compared to standard low rank training, we show that our method leads to good accuracy versus number of parameter trade-offs and can be used to speed up training of large models. For speedup, we enable faster inference on ARM processors through new open sourced kernels optimized for small batch sizes, resulting in 3x to 7x speed ups over the widely used gemmlowp library. Beyond LVCSR, we expect our techniques and kernels to be more generally applicable to embedded neural networks with large fully connected or recurrent layers.
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NNGeometry: Easy and Fast Fisher Information Matrices and Neural Tangent Kernels in PyTorch
neural tangent kernels fim ntk number nngeometry easy pytorch nngeometry useful tools
Fisher Information Matrices (FIM) and Neural Tangent Kernels (NTK) are useful tools in a number of diverse applications related to neural networks. Yet these theoretical tools are often difficult to implement using current libraries for practical size networks, given that they require per-example gradients, and a large amount of memory since they scale as the number of parameters (for the FIM) or the number of examples x cardinality of the output space (for the NTK). NNGeometry is a PyTorch library that offers a simple interface for computing various linear algebra operations such as matrix-vector products, trace, frobenius norm, and so on, where the matrix is either the FIM or the NTK, leveraging recent advances in approximating these matrices. We here present the library and motivate our design choices, then we demonstrate it on actual deep neural networks.
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Contrastive Syn-to-Real Generalization
synthetic-to-real generalization domain generalization
Training on synthetic data can be beneficial for label or data-scarce scenarios. However, synthetically trained models often suffer from poor generalization in real domains due to domain gaps. In this work, we make a key observation that the diversity of the learned feature embeddings plays an important role in the generalization performance. To this end, we propose contrastive synthetic-to-real generalization (CSG), a novel framework that leverage the pre-trained ImageNet knowledge to prevent overfitting to the synthetic domain, while promoting the diversity of feature embeddings as an inductive bias to improve generalization. In addition, we enhance the proposed CSG framework with attentional pooling (A-pool) to let the model focus on semantically important regions and further improve its generalization. We demonstrate the effectiveness of CSG on various synthetic training tasks, exhibiting state-of-the-art performance on zero-shot domain generalization.
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Continual Invariant Risk Minimization
Supervised Learning Causal Learning Invariant Risk Minimization Continual Learning
Empirical risk minimization can lead to poor generalization behaviour on unseen environments if the learned model does not capture invariant feature represen- tations. Invariant risk minimization (IRM) is a recent proposal for discovering environment-invariant representations. It was introduced by Arjovsky et al. (2019) and extended by Ahuja et al. (2020). The assumption of IRM is that all environ- ments are available to the learning system at the same time. With this work, we generalize the concept of IRM to scenarios where environments are observed se- quentially. We show that existing approaches, including those designed for contin- ual learning, fail to identify the invariant features and models across sequentially presented environments. We extend IRM under a variational Bayesian and bilevel framework, creating a general approach to continual invariant risk minimization. We also describe a strategy to solve the optimization problems using a variant of the alternating direction method of multiplier (ADMM). We show empirically us- ing multiple datasets and with multiple sequential environments that the proposed methods outperforms or is competitive with prior approaches.
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Recycling sub-optimial Hyperparameter Optimization models to generate efficient Ensemble Deep Learning
Deep Learning hyperparameter optimization ensemble deep learning multi-GPU
Ensemble Deep Learning improves accuracy over a single model by combining predictions from multiple models. It has established itself to be the core strategy for tackling the most difficult problems, like winning Kaggle challenges. Due to the lack of consensus to design a successful deep learning ensemble, we introduce Hyperband-Dijkstra, a new workflow that automatically explores neural network designs with Hyperband and efficiently combines them with Dijkstra's algorithm. This workflow has the same training cost than standard Hyperband running except sub-optimal solutions are stored and are candidates to be selected in the ensemble selection step (recycling). Next, to predict on new data, the user gives to Dijkstra the maximum number of models wanted in the ensemble to control the tradeoff between accuracy and inference time.
Hyperband is a very efficient algorithm allocating exponentially more resources to the most promising configurations. It is also capable to propose diverse models due to its pure-exploration nature, which allows Dijkstra algorithm with a smart combination of diverse models to achieve a strong variance and bias reduction. The exploding number of possible combinations generated by Hyperband increases the probability that Dijkstra finds an accurate combination which fits the dataset and generalizes on new data.
The two experimentation on CIFAR100 and on our unbalanced microfossils dataset show that our new workflow generates an ensemble far more accurate than any other ensemble of any ResNet models from ResNet18 to ResNet152.
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Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization
poisson matrix factorization generalized additive model probabilistic matrix factorization bayesian sparse coding interpretability factor analysis
Dimensionality reduction methods for count data are critical to a wide range of applications in medical informatics and other fields where model interpretability is paramount. For such data, hierarchical Poisson matrix factorization (HPF) and other sparse probabilistic non-negative matrix factorization (NMF) methods are considered to be interpretable generative models. They consist of sparse transformations for decoding their learned representations into predictions. However, sparsity in representation decoding does not necessarily imply sparsity in the encoding of representations from the original data features. HPF is often incorrectly interpreted in the literature as if it possesses encoder sparsity. The distinction between decoder sparsity and encoder sparsity is subtle but important. Due to the lack of encoder sparsity, HPF does not possess the column-clustering property of classical NMF -- the factor loading matrix does not sufficiently define how each factor is formed from the original features. We address this deficiency by self-consistently enforcing encoder sparsity, using a generalized additive model (GAM), thereby allowing one to relate each representation coordinate to a subset of the original data features. In doing so, the method also gains the ability to perform feature selection. We demonstrate our method on simulated data and give an example of how encoder sparsity is of practical use in a concrete application of representing inpatient comorbidities in Medicare patients.
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Learning representations from temporally smooth data
biologically plausible incremental learning leaky integrator multiscale hierarchical processing timescales
Events in the real world are correlated across nearby points in time, and we must learn from this temporally “smooth” data. However, when neural networks are trained to categorize or reconstruct single items, the common practice is to randomize the order of training items. What are the effects of temporally smooth training data on the efficiency of learning? We first tested the effects of smoothness in training data on incremental learning in feedforward nets and found that smoother data slowed learning. Moreover, sampling so as to minimize temporal smoothness produced more efficient learning than sampling randomly. If smoothness generally impairs incremental learning, then how can networks be modified to benefit from smoothness in the training data? We hypothesized that two simple brain-inspired mechanisms -- leaky memory in activation units and memory-gating -- could enable networks to exploit the redundancies in smooth data. Across all levels of data smoothness, these brain-inspired architectures achieved more efficient category learning than feedforward networks. Finally, we investigated how these brain-inspired mechanisms altered the internal representations learned by the networks. We found that networks with multi-scale leaky memory and memory-gating could learn internal representations that “un-mixed” data sources which vary on fast and slow timescales across training samples. Altogether, we identified simple mechanisms enabling neural networks to learn more quickly from temporally smooth data, and to generate internal representations that separate timescales in the training signal.
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Knowledge Distillation as Semiparametric Inference
knowledge distillation semiparametric inference generalization bounds model compression cross-fitting orthogonal machine learning loss correction
A popular approach to model compression is to train an inexpensive student model to mimic the class probabilities of a highly accurate but cumbersome teacher model. Surprisingly, this two-step knowledge distillation process often leads to higher accuracy than training the student directly on labeled data. To explain and enhance this phenomenon, we cast knowledge distillation as a semiparametric inference problem with the optimal student model as the target, the unknown Bayes class probabilities as nuisance, and the teacher probabilities as a plug-in nuisance estimate. By adapting modern semiparametric tools, we derive new guarantees for the prediction error of standard distillation and develop two enhancements—cross-fitting and loss correction—to mitigate the impact of teacher overfitting and underfitting on student performance. We validate our findings empirically on both tabular and image data and observe consistent improvements from our knowledge distillation enhancements.
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Model Selection for Cross-Lingual Transfer using a Learned Scoring Function
transfer model different data better models small amounts model selection multilingual text corpora
Transformers that are pre-trained on multilingual text corpora, such as, mBERT and XLM-RoBERTa, have achieved impressive cross-lingual transfer learning results. In the zero-shot cross-lingual transfer setting, only English training data is assumed, and the fine-tuned model is evaluated on another target language. No target-language validation data is assumed in this setting, however substantial variance has been observed in target language performance between different fine-tuning runs. Prior work has relied on English validation/development data to select among models that are fine-tuned with different learning rates, number of steps and other hyperparameters, often resulting in suboptimal choices. In this paper, we show that it is possible to select consistently better models when small amounts of annotated data are available in an auxiliary pivot language.
We propose a machine learning approach to model selection that uses the fine-tuned model's own internal representations to predict its cross-lingual capabilities. In extensive experiments we find that our approach consistently selects better models than English validation data across five languages and five well-studied NLP tasks, achieving results that are comparable to small amounts of target language development data.
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Visual Question Answering From Another Perspective: CLEVR Mental Rotation Tests
vqa clevr contrastive learning 3d inverse graphics
Different types of \emph{mental rotation tests} have been used extensively in psychology to understand human visual reasoning and perception. Understanding what an object or visual scene would look like from another viewpoint is a challenging problem that is made even harder if it must be performed from a single image. 3D computer vision has a long history of examining related problems. However, often what one is most interested in is the answer to a relatively simple question posed in another visual frame of reference -- as opposed to creating a full 3D reconstruction.
Mental rotations tests can also manifest as consequential questions in the real world such as: does the pedestrian that I see, see the car that I am driving?
We explore a controlled setting whereby questions are posed about the properties of a scene if the scene were observed from another viewpoint. To do this we have created a new version of the CLEVR VQA problem setup and dataset that we call CLEVR Mental Rotation Tests or CLEVR-MRT, where the goal is to answer questions about the original CLEVR viewpoint given a single image obtained from a different viewpoint of the same scene. Using CLEVR Mental Rotation Tests we examine standard state of the art methods, show how they fall short, then explore novel neural architectures that involve inferring representations encoded as feature volumes describing a scene. Our new methods use rigid transformations of feature volumes conditioned on the viewpoint camera. We examine the efficacy of different model variants through performing a rigorous ablation study. Furthermore, we examine the use of contrastive learning to infer a volumetric encoder in a self-supervised manner and find that this approach yields the best results of our study using CLEVR-MRT.
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On the Role of Pre-training for Meta Few-Shot Learning
Meta-Learning Episodic Training Pre-training Disentanglement
Few-shot learning aims to classify unknown classes of examples with a few new examples per class. There are two key routes for few-shot learning. One is to (pre-)train a classifier with examples from known classes, and then transfer the pre-trained classifier to unknown classes using the new examples. The other, called meta few-shot learning, is to couple pre-training with episodic training, which contains episodes of few-shot learning tasks simulated from the known classes. Pre-training is known to play a crucial role for the transfer route, but the role of pre-training for the episodic route is less clear. In this work, we study the role of pre-training for the episodic route. We find that pre-training serves a major role of disentangling representations of known classes, which makes the resulting learning tasks easier for episodic training. The finding allows us to shift the huge simulation burden of episodic learning to a simpler pre-training stage. We justify such a benefit of shift by designing a new disentanglement-based pre-training model, which helps episodic learning achieve competitive performance more efficiently.
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Intervention-based Recurrent causal Model for Nonstationary Video Causal Discovery
causal discovery
Nonstationary causal structures are prevalent in real-world physical systems. For example, the stacked blocks interact until they fall apart, while the billiard balls move independently until they collide. However, most video causal discovery methods can not discover such nonstationary casual structures due to the lack of modeling for the instantaneous change and the dynamics of the causal structure.
In this work, we propose the Intervention-based Recurrent Casual Model (IRCM) for nonstationary video causal discovery. First, we extend the existing intervention-based casual discovery framework for videos to formulate the instantaneous change of the causal structure in a principled manner. Then, we use a recurrent model to sequentially predict the causal structure model based on previous observations to capture the nonstationary dynamic of the causal structure.
We evaluate our method on two popular physical system simulation datasets with various types of multi-body interactions. Experiments show that the proposed IRCM achieves the state-of-the-art performance on both the counterfactual reasoning and future forecasting tasks.
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Hippocampal representations emerge when training recurrent neural networks on a memory dependent maze navigation task
recurrent neural network place cell hippocampus neural dynamics
Can neural networks learn goal-directed behaviour using similar strategies to the brain, by combining the relationships between the current state of the organism and the consequences of future actions? Recent work has shown that recurrent neural networks trained on goal based tasks can develop representations resembling those found in the brain, entorhinal cortex grid cells, for instance. Here we explore the evolution of the dynamics of their internal representations and compare this with experimental data. We observe that once a recurrent network is trained to learn the structure of its environment solely based on sensory prediction, an attractor based landscape forms in the network's representation, which parallels hippocampal place cells in structure and function. Next, we extend the predictive objective to include Q-learning for a reward task, where rewarding actions are dependent on delayed cue modulation. Mirroring experimental findings in hippocampus recordings in rodents performing the same task, this training paradigm causes nonlocal neural activity to sweep forward in space at decision points, anticipating the future path to a rewarded location. Moreover, prevalent choice and cue-selective neurons form in this network, again recapitulating experimental findings. Together, these results indicate that combining predictive, unsupervised learning of the structure of an environment with reinforcement learning can help understand the formation of hippocampus-like representations containing both spatial and task-relevant information.
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Adaptive Automotive Radar data Acquisition
Compressed Sensing Adaptive acquisition object detection
In an autonomous driving scenario, it is vital to acquire and efficiently process data from various sensors to obtain a complete and robust perspective of the surroundings. Many studies have shown the importance of having radar data in addition to images since radar improves object detection performance. We develop a novel algorithm motivated by the hypothesis that with a limited sampling budget, allocating more sampling budget to areas with the object as opposed to a uniform sampling budget ultimately improves relevant object detection and classification. In order to identify the areas with objects, we develop an algorithm to process the object detection results from the Faster R-CNN object detection algorithm and the previous radar frame and use these as prior information to adaptively allocate more bits to areas in the scene that may contain relevant objects. We use previous radar frame information to mitigate the potential information loss of an object missed by the image or the object detection network. Also, in our algorithm, the error of missing relevant information in the current frame due to the limited budget sampling of the previous radar frame did not propagate across frames. We also develop an end-to-end transformer-based 2D object detection network using the NuScenes radar and image data. Finally, we compare the performance of our algorithm against that of standard CS and adaptive CS using radar on the Oxford Radar RobotCar dataset.
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StochasTok: Improving Fine-Grained Subword Understanding in LLMs
language models pretraining tokenization
Despite impressive performance, large language models (LLMs) still struggle with seemingly simple questions such as "How many r's are in 'strawberry'?" This limitation highlights that LLMs are unable to understand how humans `see' language. We attempt to address this by experimenting with stochastic tokenization schemes in which the same text may be tokenized into multiple possible token sequences. We find that using stochastic tokenization during pretraining dramatically alters the representations learned and allows LLMs to capture understanding of fine-grained spelling-level detail in addition to the structure learned with standard tokenization. We demonstrate this by showing that LLMs pretrained with standard deterministic tokenization cannot be fine-tuned to answer language-game type questions, whilst with the minimal addition of stochastic tokenization during pretraining, the corresponding LLMs perform near-perfectly. Crucially, these improvements are achieved without any performance drop on standard benchmarks or any additional training cost — the only change is a single simple, computationally cheap preprocessing step. Overall, our results suggest that embracing stochastic tokenization can help enable LLMs to better understand how humans perceive language.
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Generalizing and Tensorizing Subgraph Search in the Supernet
deep learning neural architecture search tensor decomposition
Recently, a special kind of graph, i.e., supernet, which allows two nodes connected by multi-choice edges, has exhibited its power in neural architecture search (NAS) by searching better architectures for computer vision (CV) and natural language processing (NLP) tasks. In this paper, we discover that the design of such discrete architectures also appears in many other important learning tasks, e.g., logical chain inference in knowledge graphs (KGs) and meta-path discovery in heterogeneous information networks (HINs). Thus, we are motivated to generalize the supernet search problem on a broader horizon. However, none of the existing works are effective since the supernet's topology is highly task-dependent and diverse. To address this issue, we propose to tensorize the supernet, i.e. unify the subgraph search problems by a tensor formulation and encode the topology inside the supernet by a tensor network. We further propose an efficient algorithm that admits both stochastic and deterministic objectives to solve the search problem. Finally, we perform extensive experiments on diverse learning tasks, i.e., architecture design for CV, logic inference for KG, and meta-path discovery for HIN. Empirical results demonstrate that our method leads to better performance and architectures.
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FSV: Learning to Factorize Soft Value Function for Cooperative Multi-Agent Reinforcement Learning
cooperative MARL value function factorization stochastic policy continuous tasks
We explore energy-based solutions for cooperative multi-agent reinforcement learning (MARL) using the idea of function factorization in centralized training with decentralized execution (CTDE). Existing CTDE based factorization methods are susceptible to the relative overgeneralization, where finding a suboptimal Nash Equilibrium, which is a well-known game-theoretic pathology. To resolve this issue, we propose a novel factorization method for cooperative MARL, named FSV, which learns to factorize the joint soft value function into individual ones for decentralized execution. Theoretical analysis shows that FSV solves a rich class of factorization tasks. Our experiment for the well-known task of the Max of Two Quadratics game shows that FSV fully converges to global optima in the joint action space in the continuous tasks by local searching in the joint action space. We evaluate FSV on a challenging set of StarCraft II micromanagement tasks, and show that FSV significantly outperforms existing factorization multi-agent reinforcement learning methods.
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Learning Associative Inference Using Fast Weight Memory
memory-augmented neural networks tensor product fast weights
Humans can quickly associate stimuli to solve problems in novel contexts. Our novel neural network model learns state representations of facts that can be composed to perform such associative inference. To this end, we augment the LSTM model with an associative memory, dubbed \textit{Fast Weight Memory} (FWM). Through differentiable operations at every step of a given input sequence, the LSTM \textit{updates and maintains} compositional associations stored in the rapidly changing FWM weights. Our model is trained end-to-end by gradient descent and yields excellent performance on compositional language reasoning problems, meta-reinforcement-learning for POMDPs, and small-scale word-level language modelling.
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Simple Spectral Graph Convolution
Graph Convolutional Network Oversmoothing
Graph Convolutional Networks (GCNs) are leading methods for learning graph representations. However, without specially designed architectures, the performance of GCNs degrades quickly with increased depth. As the aggregated neighborhood size and neural network depth are two completely orthogonal aspects of graph representation, several methods focus on summarizing the neighborhood by aggregating K-hop neighborhoods of nodes while using shallow neural networks. However, these methods still encounter oversmoothing, and suffer from high computation and storage costs. In this paper, we use a modified Markov Diffusion Kernel to derive a variant of GCN called Simple Spectral Graph Convolution (SSGC). Our spectral analysis shows that our simple spectral graph convolution used in SSGC is a trade-off of low- and high-pass filter bands which capture the global and local contexts of each node. We provide two theoretical claims which demonstrate that we can aggregate over a sequence of increasingly larger neighborhoods compared to competitors while limiting severe oversmoothing. Our experimental evaluations show that SSGC with a linear learner is competitive in text and node classification tasks. Moreover, SSGC is comparable to other state-of-the-art methods for node clustering and community prediction tasks.
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Why Does DARTS Miss the Target, and How Do We Aim to Fix It?
NAS DARTS neural architecture search neural networks deep learning differentiable NAS differentiable neural architecture search
This blog post is based on 'Rethinking Architecture Selection in Differentiable NAS' from ICLR 2021 (Wang et al., 2021). The post establishes context by explaining DARTS (Liu et al., 2019) and summarizes an analysis of the failure modes of DARTS (Zela et al., 2020) before returning to focus on the main work. The post compares potential causes for failure of DARTS presented by (Zela et al., 2020) and (Wang et al., 2021) before describing the perturbation algorithm presented in (Wang et al., 2021) in order to help remedy failings of DARTS. Finally, the post reflects on the perturbation algorithm's downside as a step away from differentiable NAS (and toward discrete NAS) and relates the supernet-based pruning of DARTS to broader literature in network pruning.
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Efficient Robust Training via Backward Smoothing
Efficient Robust Training Backward Smoothing Robustness
Adversarial training is so far the most effective strategy in defending against adversarial examples. However, it suffers from high computational cost due to the iterative adversarial attacks in each training step. Recent studies show that it is possible to achieve Fast Adversarial Training by performing a single-step attack with random initialization. Yet, it remains a mystery why random initialization helps. Besides, such an approach still lags behind state-of-the-art adversarial training algorithms on both stability and model robustness. In this work, we develop a new understanding towards Fast Adversarial Training, by viewing random initialization as performing randomized smoothing for better optimization of the inner maximization problem. From this perspective, we show that the smoothing effect by random initialization is not sufficient under the adversarial perturbation constraint. A new initialization strategy, \emph{backward smoothing}, is proposed to address this issue and significantly improves both stability and model robustness over single-step robust training methods. Experiments on multiple benchmarks demonstrate that our method achieves similar model robustness as the original TRADES method, while using much less training time (~3x improvement with the same training schedule).
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Learning from Samples of Variable Quality
fidelity-weighted learning semisupervised learning weakly-labeled data teacher-student
Training labels are expensive to obtain and may be of varying quality, as some may be from trusted expert labelers while others might be from heuristics or other sources of weak supervision such as crowd-sourcing. This creates a fundamental quality-versus-quantity trade-off in the learning process. Do we learn from the small amount of high-quality data or the potentially large amount of weakly-labeled data? We argue that if the learner could somehow know and take the label-quality into account, we could get the best of both worlds. To this end, we introduce “fidelity-weighted learning” (FWL), a semi-supervised student-teacher approach for training deep neural networks using weakly-labeled data. FWL modulates the parameter updates to a student network, trained on the task we care about on a per-sample basis according to the posterior confidence of its label-quality estimated by a teacher, who has access to limited samples with high-quality labels.
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Accelerating DNN Training through Selective Localized Learning
Efficient DNN Training
Training Deep Neural Networks (DNNs) places immense compute requirements on the underlying hardware platforms, expending large amounts of time and energy. We proposeLoCal+SGD, a new algorithmic approach to accelerate DNN train-ing by selectively combining localized or Hebbian learning within a StochasticGradient Descent (SGD) based training framework. Back-propagation is a computationally expensive process that requires 2 Generalized Matrix Multiply (GEMM)operations to compute the error and weight gradients for each layer. We alleviate this by selectively updating some layers’ weights using localized learning rules that require only 1 GEMM operation per layer. Further, since the weight update is performed during the forward pass itself, the layer activations for the mini-batch do not need to be stored until the backward pass, resulting in a reduced memory footprint. Localized updates can substantially boost training speed, but need to be used selectively and judiciously in order to preserve accuracy and convergence. We address this challenge through the design of a Learning Mode Selection Algorithm, where all layers start with SGD, and as epochs progress, layers gradually transition to localized learning. Specifically, for each epoch, the algorithm identifies a Localized→SGDtransition layer, which delineates the network into two regions. Layers before the transition layer use localized updates, while the transition layer and later layers use gradient-based updates. The trend in the weight updates made to the transition layer across epochs is used to determine how the boundary betweenSGD and localized updates is shifted in future epochs. We also propose a low-cost weak supervision mechanism by controlling the learning rate of localized updates based on the overall training loss. We applied LoCal+SGDto 8 image recognition CNNs (including ResNet50 and MobileNetV2) across 3 datasets (Cifar10, Cifar100and ImageNet). Our measurements on a Nvidia GTX 1080Ti GPU demonstrate upto 1.5×improvement in end-to-end training time with∼0.5% loss in Top-1classification accuracy.
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Representation learning for improved interpretability and classification accuracy of clinical factors from EEG
EEG ERP electroencephalography depression representation learning disentanglement beta-VAE
Despite extensive standardization, diagnostic interviews for mental health disorders encompass substantial subjective judgment. Previous studies have demonstrated that EEG-based neural measures can function as reliable objective correlates of depression, or even predictors of depression and its course. However, their clinical utility has not been fully realized because of 1) the lack of automated ways to deal with the inherent noise associated with EEG data at scale, and 2) the lack of knowledge of which aspects of the EEG signal may be markers of a clinical disorder. Here we adapt an unsupervised pipeline from the recent deep representation learning literature to address these problems by 1) learning a disentangled representation using $\beta$-VAE to denoise the signal, and 2) extracting interpretable features associated with a sparse set of clinical labels using a Symbol-Concept Association Network (SCAN). We demonstrate that our method is able to outperform the canonical hand-engineered baseline classification method on a number of factors, including participant age and depression diagnosis. Furthermore, our method recovers a representation that can be used to automatically extract denoised Event Related Potentials (ERPs) from novel, single EEG trajectories, and supports fast supervised re-mapping to various clinical labels, allowing clinicians to re-use a single EEG representation regardless of updates to the standardized diagnostic system. Finally, single factors of the learned disentangled representations often correspond to meaningful markers of clinical factors, as automatically detected by SCAN, allowing for human interpretability and post-hoc expert analysis of the recommendations made by the model.
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Uncertainty Calibration Error: A New Metric for Multi-Class Classification
variational inference uncertainty calibration classification
Various metrics have recently been proposed to measure uncertainty calibration of deep models for classification. However, these metrics either fail to capture miscalibration correctly or lack interpretability. We propose to use the normalized entropy as a measure of uncertainty and derive the Uncertainty Calibration Error (UCE), a comprehensible calibration metric for multi-class classification. In our experiments, we focus on uncertainty from variational Bayesian inference methods and compare UCE to established calibration errors on the task of multi-class image classification. UCE avoids several pathologies of other metrics, but does not sacrifice interpretability. It can be used for regularization to improve calibration during training without penalizing predictions with justified high confidence.
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Deep $k$-NN Label Smoothing Improves Reproducibility of Neural Network Predictions
$k$-nearest neighbors neural networks label smoothing churn reproducibility stability robustness
Training modern neural networks is an inherently noisy process that can lead to high \emph{prediction churn}-- disagreements between re-trainings of the same model due to factors such as randomization in the parameter initialization and mini-batches-- even when the trained models all attain high accuracies. Such prediction churn can be very undesirable in practice. In this paper, we present several baselines for reducing churn and show that utilizing the $k$-NN predictions to smooth the labels results in a new and principled method that often outperforms the baselines on churn while improving accuracy on a variety of benchmark classification tasks and model architectures.
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PDEformer: Towards a Foundation Model for One-Dimensional Partial Differential Equations
partial differential equation (PDE) foundation model computational graph implicit neural representation (INR)
This paper introduces PDEformer, a neural solver for partial differential equations (PDEs) capable of simultaneously addressing various types of PDEs. We propose to represent the PDE in the form of a computational graph, facilitating the seamless integration of both symbolic and numerical information inherent in a PDE. A graph Transformer and an implicit neural representation (INR) are employed to generate mesh-free predicted solutions. Following pretraining on data exhibiting a certain level of diversity, our model achieves zero-shot accuracies on benchmark datasets that is comparable to those of specifically trained expert models. Additionally, PDEformer demonstrates promising results in the inverse problem of PDE coefficient recovery.
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Low Complexity Approximate Bayesian Logistic Regression for Sparse Online Learning
Bayesian methods logistic regression regret online learning MDL.
Theoretical results show that Bayesian methods can achieve lower bounds on regret for online logistic regression. In practice, however, such techniques may not be feasible especially for very large feature sets. Various approximations that, for huge sparse feature sets, diminish the theoretical advantages, must be used. Often, they apply stochastic gradient methods with hyper-parameters that must be tuned on some surrogate loss, defeating theoretical advantages of Bayesian methods. The surrogate loss, defined to approximate the mixture, requires techniques as Monte Carlo sampling, increasing computations per example. We propose low complexity analytical approximations for sparse online logistic and probit regressions. Unlike variational inference and other methods, our methods use analytical closed forms, substantially lowering computations. Unlike dense solutions,
as Gaussian Mixtures, our methods allow for sparse problems with huge feature sets without increasing complexity. With the analytical closed forms, there is also no need for applying stochastic gradient methods on surrogate losses, and for tuning and balancing learning and regularization hyper-parameters. Empirical results top the performance of the more computationally involved methods. Like such methods, our methods still reveal per feature and per example uncertainty measures.
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Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization
early phase of training implicit regularization SGD learning rate batch size Hessian Fisher Information Matrix curvature gradient norm
The early phase of training has been shown to be important in two ways for deep neural networks. First, the degree of regularization in this phase significantly impacts the final generalization. Second, it is accompanied by a rapid change in the local loss curvature influenced by regularization choices. Connecting these two findings, we show that stochastic gradient descent (SGD) implicitly penalizes the trace of the Fisher Information Matrix (FIM) from the beginning of training. We argue it is an implicit regularizer in SGD by showing that explicitly penalizing the trace of the FIM can significantly improve generalization. We further show that the early value of the trace of the FIM correlates strongly with the final generalization. We highlight that in the absence of implicit or explicit regularization, the trace of the FIM can increase to a large value early in training, to which we refer as catastrophic Fisher explosion. Finally, to gain insight into the regularization effect of penalizing the trace of the FIM, we show that it limits memorization by reducing the learning speed of examples with noisy labels more than that of the clean examples, and 2) trajectories with a low initial trace of the FIM end in flat minima, which are commonly associated with good generalization.
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PODS: Policy Optimization via Differentiable Simulation
Reinforcement Learning Decision and Control Planning Robotics.
Current reinforcement learning (RL) methods use simulation models as simple black-box oracles. In this paper, with the goal of improving the performance exhibited by RL algorithms, we explore a systematic way of leveraging the additional information provided by an emerging class of differentiable simulators. Building on concepts established by Deterministic Policy Gradients (DPG) methods, the neural network policies learned with our approach represent deterministic actions. In a departure from standard methodologies, however, learning these policy does not hinge on approximations of the value function that must be learned concurrently in an actor-critic fashion. Instead, we exploit differentiable simulators to directly compute the analytic gradient of a policy's value function with respect to the actions it outputs. This, in turn, allows us to efficiently perform locally optimal policy improvement iterations. Compared against other state-of-the-art RL methods, we show that with minimal hyper-parameter tuning our approach consistently leads to better asymptotic behavior across a set of payload manipulation tasks that demand high precision.
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Improving Model Robustness with Latent Distribution Locally and Globally
adversarial example robustness data manifold adversarial training
We propose a novel adversarial training method which leverages both the local and global information to defend adversarial attacks. Existing adversarial training methods usually generate adversarial perturbations locally in a supervised manner and fail to consider the data manifold information in a global way. Consequently, the resulting adversarial examples may corrupt the underlying data structure and are typically biased towards the decision boundary. In this work, we exploit both the local and global information of data manifold to generate adversarial examples in an unsupervised manner. Specifically, we design our novel framework via an adversarial game between a discriminator and a classifier: the discriminator is learned to differentiate the latent distributions of the natural data and the perturbed counterpart, while the classifier is trained to recognize accurately the perturbed examples as well as enforcing the invariance between the two latent distributions. We conduct a series of analysis on the model robustness and also verify the effectiveness of our proposed method empirically. Experimental results show that our method substantially outperforms the recent state-of-the-art (i.e. Feature Scattering) in defending adversarial attacks by a large accuracy margin (e.g. $17.0\%$ and $18.1\%$ on SVHN dataset, $9.3\%$ and $17.4\%$ on CIFAR-10 dataset, $6.0\%$ and $16.2\%$ on CIFAR-100 dataset for defending PGD20 and CW20 attacks respectively).
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Rule and Convention Transfer in Context
Human AI Collaboration Multi Agent Conventions
We discuss context and other work surrounding conventions in Human-AI Collaboration, motivated by Shih et al.'s "On the Critical Role of Conventions in Adaptive Human-AI Collaboration", published at ICLR 2021. We also discuss some room for improvement in the usage of conventions in Human-AI Collaboration.
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Blending MPC & Value Function Approximation for Efficient Reinforcement Learning
reinforcement learning model-predictive control
Model-Predictive Control (MPC) is a powerful tool for controlling complex, real-world systems that uses a model to make predictions about future behavior. For each state encountered, MPC solves an online optimization problem to choose a control action that will minimize future cost. This is a surprisingly effective strategy, but real-time performance requirements warrant the use of simple models. If the model is not sufficiently accurate, then the resulting controller can be biased, limiting performance. We present a framework for improving on MPC with model-free reinforcement learning (RL). The key insight is to view MPC as constructing a series of local Q-function approximations. We show that by using a parameter $\lambda$, similar to the trace decay parameter in TD($\lambda$), we can systematically trade-off learned value estimates against the local Q-function approximations. We present a theoretical analysis that shows how error from inaccurate models in MPC and value function estimation in RL can be balanced. We further propose an algorithm that changes $\lambda$ over time to reduce the dependence on MPC as our estimates of the value function improve, and test the efficacy our approach on challenging high-dimensional manipulation tasks with biased models in simulation. We demonstrate that our approach can obtain performance comparable with MPC with access to true dynamics even under severe model bias and is more sample efficient as compared to model-free RL.
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Sequence Metric Learning as Synchronization of Recurrent Neural Networks
Metric learning sequence processing siamese recurrent neural network dynamical systems
Sequence metric learning is becoming a widely adopted approach for various applications dealing with sequential multi-variate data such as activity recognition or natural language processing and is most of the time tackled with sequence alignment approaches or representation learning.
In this paper, we propose to study this subject from the point of view of dynamical system theory by drawing the analogy between synchronized trajectories produced by dynamical systems and the distance between similar sequences processed by a siamese recurrent neural network.
Indeed, a siamese recurrent network comprises two identical sub-networks, two identical dynamical systems which can theoretically achieve complete synchronization if a coupling is introduced between them.
We therefore propose a new neural network model that implements this coupling with a new gate integrated into the classical Gated Recurrent Unit architecture. This model is thus able to simultaneously learn a similarity metric and the synchronization of unaligned multi-variate sequences in a weakly supervised way.
Our experiments show that introducing such a coupling improves the performance of the siamese Gated Recurrent Unit architecture on an activity recognition dataset.
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Correlated Variational Auto-Encoders
variational latent representations vaes correlations correlation structure capable high dimensional data due assumption singleton variational distributions
Variational Auto-Encoders (VAEs) are capable of learning latent representations for high dimensional data. However, due to the i.i.d. assumption, VAEs only optimize the singleton variational distributions and fail to account for the correlations between data points, which might be crucial for learning latent representations from dataset where a priori we know correlations exist. We propose Correlated Variational Auto-Encoders (CVAEs) that can take the correlation structure into consideration when learning latent representations with VAEs. CVAEs apply a prior based on the correlation structure. To address the intractability introduced by the correlated prior, we develop an approximation by average of a set of tractable lower bounds over all maximal acyclic subgraphs of the undirected correlation graph. Experimental results on matching and link prediction on public benchmark rating datasets and spectral clustering on a synthetic dataset show the effectiveness of the proposed method over baseline algorithms.
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The Differences Between Direct Alignment Algorithms are a Blur
direct alignment algorithms large language models preference optimization
Direct Alignment Algorithms (DAAs) simplify language model alignment by replacing reinforcement learning (RL) and reward modeling (RM) in Reinforcement Learning from Human Feedback (RLHF) with direct policy optimization. DAAs can be classified by their ranking losses (pairwise vs. pointwise), by the rewards used in those losses (e.g., likelihood ratios of policy and reference policy, or odds ratios), or by whether a Supervised Fine-Tuning (SFT) phase is required (two-stage vs. one-stage). We first show that one-stage methods underperform two-stage methods. To address this, we incorporate an explicit SFT phase and introduce the $\beta$ parameter, controlling the strength of preference optimization, into single-stage ORPO and ASFT. These modifications improve their performance in Alpaca Eval 2 by +$3.46$ (ORPO) and +$8.27$ (ASFT), matching two-stage methods like DPO. Further analysis reveals that the key factor is whether the approach uses pairwise or pointwise objectives, rather than the specific implicit reward or loss function. These results highlight the importance of careful evaluation to avoid premature claims of performance gains or overall superiority in alignment algorithms.
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Generating Diverse High-Resolution Images with VQ-VAE
Vector Quantization Autoregressive models Generative Models
We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation. To this end, we scale and enhance the autoregressive priors used in VQ-VAE to generate synthetic samples of much higher coherence and fidelity than possible before. We use simple feed-forward encoder and decoder networks, thus our model is an attractive candidate for applications where the encoding and decoding speed is critical. Additionally, this allows us to only sample autoregressively in the compressed latent space, which is an order of magnitude faster than sampling in the pixel space, especially for large images. We demonstrate that a multi-scale hierarchical organization of VQ-VAE, augmented with powerful priors over the latent codes, is able to generate samples with quality that rivals that of state of the art Generative Adversarial Networks on multifaceted datasets such as ImageNet, while not suffering from GAN's known shortcomings such as mode collapse and lack of diversity.
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Multiplicative Filter Networks
Deep Architectures Implicit Neural Representations Fourier Features
Although deep networks are typically used to approximate functions over high dimensional inputs, recent work has increased interest in neural networks as function approximators for low-dimensional-but-complex functions, such as representing images as a function of pixel coordinates, solving differential equations, or representing signed distance fields or neural radiance fields. Key to these recent successes has been the use of new elements such as sinusoidal nonlinearities, or Fourier features in positional encodings, which vastly outperform simple ReLU networks. In this paper, we propose and empirically demonstrate that an arguably simpler class of function approximators can work just as well for such problems: multiplicative filter networks. In these networks, we avoid traditional compositional depth altogether, and simply multiply together (linear functions of) sinusoidal or Gabor wavelet functions applied to the input. This representation has the notable advantage that the entire function can simply be viewed as a linear function approximator over an exponential number of Fourier or Gabor basis functions, respectively. Despite this simplicity, when compared to recent approaches that use Fourier features with ReLU networks or sinusoidal activation networks, we show that these multiplicative filter networks largely outperform or match the performance of these recent approaches on the domains highlighted in these past works.
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Reducing the number of neurons of Deep ReLU Networks based on the current theory of Regularization
Reduction Compression Regularization Theory Pruning Deep Interpretability Generalization
We introduce a new Reduction Algorithm which makes use of the properties of ReLU neurons to reduce significantly the number of neurons in a trained Deep Neural Network. This algorithm is based on the recent theory of implicit and explicit regularization in Deep ReLU Networks from (Maennel et al, 2018) and the authors.
We discuss two experiments which illustrate the efficiency of the algorithm to reduce the number of neurons significantly with provably almost no change of the learned function within the training data (and therefore almost no loss in accuracy).
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On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines
fine-tuning stability transfer learning pretrained language model BERT
Fine-tuning pre-trained transformer-based language models such as BERT has become a common practice dominating leaderboards across various NLP benchmarks. Despite the strong empirical performance of fine-tuned models, fine-tuning is an unstable process: training the same model with multiple random seeds can result in a large variance of the task performance. Previous literature (Devlin et al., 2019; Lee et al., 2020; Dodge et al., 2020) identified two potential reasons for the observed instability: catastrophic forgetting and small size of the fine-tuning datasets. In this paper, we show that both hypotheses fail to explain the fine-tuning instability. We analyze BERT, RoBERTa, and ALBERT, fine-tuned on commonly used datasets from the GLUE benchmark, and show that the observed instability is caused by optimization difficulties that lead to vanishing gradients. Additionally, we show that the remaining variance of the downstream task performance can be attributed to differences in generalization where fine-tuned models with the same training loss exhibit noticeably different test performance. Based on our analysis, we present a simple but strong baseline that makes fine-tuning BERT-based models significantly more stable than the previously proposed approaches. Code to reproduce our results is available online: https://github.com/uds-lsv/bert-stable-fine-tuning.
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Improved Contrastive Divergence Training of Energy Based Models
Contrastive Divergence Energy Based Modeling
We propose several different techniques to improve contrastive divergence training of energy-based models (EBMs). We first show that a gradient term neglected in the popular contrastive divergence formulation is both tractable to estimate and is important to avoid training instabilities in previous models. We further highlight how data augmentation, multi-scale processing, and reservoir sampling can be used to improve model robustness and generation quality. Thirdly, we empirically evaluate stability of model architectures and show improved performance on a host of benchmarks and use cases, such as image generation, OOD detection, and compositional generation.
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Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods
Excess risk minimax optimal rate local Rademacher complexity fast learning rate kernel method linear estimator
Establishing a theoretical analysis that explains why deep learning can outperform shallow learning such as kernel methods is one of the biggest issues in the deep learning literature. Towards answering this question, we evaluate excess risk of a deep learning estimator trained by a noisy gradient descent with ridge regularization on a mildly overparameterized neural network,
and discuss its superiority to a class of linear estimators that includes neural tangent kernel approach, random feature model, other kernel methods, $k$-NN estimator and so on. We consider a teacher-student regression model, and eventually show that {\it any} linear estimator can be outperformed by deep learning in a sense of the minimax optimal rate especially for a high dimension setting. The obtained excess bounds are so-called fast learning rate which is faster than $O(1/\sqrt{n})$ that is obtained by usual Rademacher complexity analysis. This discrepancy is induced by the non-convex geometry of the model and the noisy gradient descent used for neural network training provably reaches a near global optimal solution even though the loss landscape is highly non-convex. Although the noisy gradient descent does not employ any explicit or implicit sparsity inducing regularization, it shows a preferable generalization performance that dominates linear estimators.
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Shuffle to Learn: Self-supervised learning from permutations via differentiable ranking
permutations learning shuffle differentiable tasks classification pretext impressive performance wide range work
Self-supervised pre-training using so-called "pretext" tasks has recently shown impressive performance across a wide range of tasks. In this work we advance self-supervised learning from permutations, that consists in shuffling parts of input and training a model to reorder them, improving downstream performance in classification. To do so, we overcome the main challenges of integrating permutation inversions (a discontinuous operation) into an end-to-end training scheme, heretofore sidestepped by casting the reordering task as classification, fundamentally reducing the space of permutations that can be exploited. These advances rely on two main, independent contributions. First, we use recent advances in differentiable ranking to integrate the permutation inversion flawlessly into a neural network, enabling us to use the full set of permutations, at no additional computing cost. Our experiments validate that learning from all possible permutations (up to $10^{18}$) improves the quality of the pre-trained representations over using a limited, fixed set. Second, we successfully demonstrate that inverting permutations is a meaningful pretext task in a diverse range of modalities, beyond images, which does not require modality-specific design. In particular, we also improve music understanding by reordering spectrogram patches in the frequency space, as well as video classification by reordering frames along the time axis. We furthermore analyze the influence of the patches that we use (vertical, horizontal, 2-dimensional), as well as the benefit of our approach in different data regimes.
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The inductive bias of ReLU networks on orthogonally separable data
inductive bias implicit bias gradient descent ReLU networks max-margin extremal sector
We study the inductive bias of two-layer ReLU networks trained by gradient flow. We identify a class of easy-to-learn (`orthogonally separable') datasets, and characterise the solution that ReLU networks trained on such datasets converge to. Irrespective of network width, the solution turns out to be a combination of two max-margin classifiers: one corresponding to the positive data subset and one corresponding to the negative data subset.
The proof is based on the recently introduced concept of extremal sectors, for which we prove a number of properties in the context of orthogonal separability. In particular, we prove stationarity of activation patterns from some time $T$ onwards, which enables a reduction of the ReLU network to an ensemble of linear subnetworks.
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Plan-Based Relaxed Reward Shaping for Goal-Directed Tasks
reinforcement learning reward shaping plan-based reward shaping robotics robotic manipulation
In high-dimensional state spaces, the usefulness of Reinforcement Learning (RL) is limited by the problem of exploration. This issue has been addressed using potential-based reward shaping (PB-RS) previously. In the present work, we introduce Final-Volume-Preserving Reward Shaping (FV-RS). FV-RS relaxes the strict optimality guarantees of PB-RS to a guarantee of preserved long-term behavior. Being less restrictive, FV-RS allows for reward shaping functions that are even better suited for improving the sample efficiency of RL algorithms. In particular, we consider settings in which the agent has access to an approximate plan. Here, we use examples of simulated robotic manipulation tasks to demonstrate that plan-based FV-RS can indeed significantly improve the sample efficiency of RL over plan-based PB-RS.
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A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks
PAC Bayes Generalization Bounds Graph Neural Networks Graph Convolutional Neural Networks Message Passing GNNs
In this paper, we derive generalization bounds for two primary classes of graph neural networks (GNNs), namely graph convolutional networks (GCNs) and message passing GNNs (MPGNNs), via a PAC-Bayesian approach. Our result reveals that the maximum node degree and the spectral norm of the weights govern the generalization bounds of both models. We also show that our bound for GCNs is a natural generalization of the results developed in \citep{neyshabur2017pac} for fully-connected and convolutional neural networks. For MPGNNs, our PAC-Bayes bound improves over the Rademacher complexity based bound \citep{garg2020generalization}, showing a tighter dependency on the maximum node degree and the maximum hidden dimension. The key ingredients of our proofs are a perturbation analysis of GNNs and the generalization of PAC-Bayes analysis to non-homogeneous GNNs. We perform an empirical study on several synthetic and real-world graph datasets and verify that our PAC-Bayes bound is tighter than others.
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INCORPORATING BILINGUAL DICTIONARIES FOR LOW RESOURCE SEMI-SUPERVISED NEURAL MACHINE TRANSLATION
bilingual dictionaries neural machine translation low resource ways conventional methods success quality models
We explore ways of incorporating bilingual dictionaries to enable semi-supervised
neural machine translation. Conventional back-translation methods have shown
success in leveraging target side monolingual data. However, since the quality of
back-translation models is tied to the size of the available parallel corpora, this
could adversely impact the synthetically generated sentences in a low resource
setting. We propose a simple data augmentation technique to address both this
shortcoming. We incorporate widely available bilingual dictionaries that yield
word-by-word translations to generate synthetic sentences. This automatically
expands the vocabulary of the model while maintaining high quality content. Our
method shows an appreciable improvement in performance over strong baselines.
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A Strong On-Policy Competitor To PPO
proximal policy optimization deep reinforcement learning
As a recognized variant and improvement for Trust Region Policy Optimization (TRPO), proximal policy optimization (PPO) has been widely used with several advantages: efficient data utilization, easy implementation and good parallelism. In this paper, a first-order gradient on-policy learning algorithm called Policy Optimization with Penalized Point Probability Distance (POP3D), which is a lower bound to the square of total variance divergence is proposed as another powerful variant. The penalty item has dual effects, prohibiting policy updates from overshooting and encouraging more explorations. Carefully controlled experiments on both discrete and continuous benchmarks verify our approach is highly competitive to PPO.
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Outlier Robust Optimal Transport
Optimal transport outliers robustness
Optimal transport (OT) provides a way of measuring distances between distributions that depends on the geometry of the sample space. In light of recent advances in solving the OT problem, OT distances are widely used as loss functions in minimum distance estimation. Despite its prevalence and advantages, however, OT is extremely sensitive to outliers. A single adversarially-picked outlier can increase OT distance arbitrarily. To address this issue, in this work we propose an outlier-robust OT formulation. Our formulation is convex but challenging to scale at a first glance. We proceed by deriving an equivalent formulation based on cost truncation that is easy to incorporate into modern stochastic algorithms for regularized OT. We demonstrate our model applied to mean estimation under the Huber contamination model in simulation as well as outlier detection on real data.
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Recursive Neighborhood Pooling for Graph Representation Learning
gnns expressive power recursive neighborhood graph representation massage graph neural networks popular architectures graphs recent works important shortcomings
While massage passing based Graph Neural Networks (GNNs) have become increasingly popular architectures for learning with graphs, recent works have revealed important shortcomings in their expressive power. In response, several higher-order GNNs have been proposed, which substantially increase the expressive power, but at a large computational cost.
Motivated by this gap, we introduce and analyze a new recursive pooling technique of local neighborhoods that allows different tradeoffs of computational cost and expressive power. First, we show that this model can count subgraphs of size $k$, and thereby overcomes a known limitation of low-order GNNs. Second, we prove that, in several cases, RNP-GNNs can greatly reduce computational complexity compared to the existing higher-order $k$-GNN and Local Relational Pooling (LRP) networks.
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Coordinated Multi-Agent Exploration Using Shared Goals
Multi-agent RL Deep RL Exploration
Exploration is critical for good results of deep reinforcement learning algorithms and has drawn much attention. However, existing multi-agent deep reinforcement learning algorithms still use mostly noise-based techniques. It was recognized recently that noise-based exploration is suboptimal in multi-agent settings, and exploration methods that consider agents' cooperation have been developed. However, existing methods suffer from a common challenge: agents struggle to identify states that are worth exploring, and don't coordinate their exploration efforts toward those states. To address this shortcoming, in this paper, we proposed coordinated multi-agent exploration (CMAE): agents share a common goal while exploring. The goal is selected by a normalized entropy-based technique from multiple projected state spaces. Then, agents are trained to reach the goal in a coordinated manner. We demonstrated that our approach needs only $1\%-5\%$ of the environment steps to achieve similar or better returns than state-of-the-art baselines on various sparse-reward tasks, including a sparse-reward version of the Starcraft multi-agent challenge (SMAC).
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Uniform-Precision Neural Network Quantization via Neural Channel Expansion
deep neural network quantization neural architecture search image classification reduced precision inference
Uniform-precision neural network quantization has gained popularity thanks to its simple arithmetic unit densely packed for high computing capability. However, it ignores heterogeneous sensitivity to the impact of quantization across the layers, resulting in sub-optimal inference accuracy. This work proposes a novel approach to adjust the network structure to alleviate the impact of uniform-precision quantization. The proposed neural architecture search selectively expands channels for the quantization sensitive layers while satisfying hardware constraints (e.g., FLOPs). We provide substantial insights and empirical evidence that the proposed search method called neural channel expansion can adapt several popular networks' channels to achieve superior 2-bit quantization accuracy on CIFAR10 and ImageNet. In particular, we demonstrate the best-to-date Top-1/Top-5 accuracy for 2-bit ResNet50 with smaller FLOPs and the parameter size.
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Generalizing Graph Convolutional Networks via Heat Kernel
graph networks
Graph convolutional networks (GCNs) have emerged as a powerful framework for mining and learning with graphs. A recent study shows that GCNs can be simplified as a linear model by removing nonlinearities and weight matrices across all consecutive layers, resulting the simple graph convolution (SGC) model. In this paper, we aim to understand GCNs and generalize SGC as a linear model via heat kernel (HKGCN), which acts as a low-pass filter on graphs and enables the aggregation of information from extremely large receptive fields. We theoretically show that HKGCN is in nature a continuous propagation model and GCNs without nonlinearities (i.e., SGC) are the discrete versions of it. Its low-pass filter and continuity properties facilitate the fast and smooth convergence of feature propagation. Experiments on million-scale networks show that the linear HKGCN model not only achieves consistently better results than SGC but also can match or even beat advanced GCN models, while maintaining SGC’s superiority in efficiency.
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Self-Organizing Intelligent Matter: A blueprint for an AI generating algorithm
Artificial Life AI Generating Algorithms
We propose an artificial life framework aimed at facilitating the emergence of intelligent organisms. In this framework there is no explicit notion of an agent: instead there is an environment made of atomic elements. These elements contain neural operations and interact through exchanges of information and through physics-like rules contained in the environment. We discuss how an evolutionary process can lead to the emergence of different organisms made of many such atomic elements which can coexist and thrive in the environment. We discuss how this forms the basis of a general AI generating algorithm. We provide a simplified implementation of such system and discuss what advances need to be made to scale it up further.
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Predicting Inductive Biases of Pre-Trained Models
information-theoretical probing probing challenge sets natural language processing
Most current NLP systems are based on a pre-train-then-fine-tune paradigm, in which a large neural network is first trained in a self-supervised way designed to encourage the network to extract broadly-useful linguistic features, and then fine-tuned for a specific task of interest. Recent work attempts to understand why this recipe works and explain when it fails. Currently, such analyses have produced two sets of apparently-contradictory results. Work that analyzes the representations that result from pre-training (via "probing classifiers") finds evidence that rich features of linguistic structure can be decoded with high accuracy, but work that analyzes model behavior after fine-tuning (via "challenge sets") indicates that decisions are often not based on such structure but rather on spurious heuristics specific to the training set. In this work, we test the hypothesis that the extent to which a feature influences a model's decisions can be predicted using a combination of two factors: The feature's "extractability" after pre-training (measured using information-theoretic probing techniques), and the "evidence" available during fine-tuning (defined as the feature's co-occurrence rate with the label). In experiments with both synthetic and natural language data, we find strong evidence (statistically significant correlations) supporting this hypothesis.
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Joint autoencoders: a flexible meta-learning framework
transfer learning domain adaptation unsupervised learning autoencoders multi-task learning
The incorporation of prior knowledge into learning is essential in achieving good performance based on small noisy samples. Such knowledge is often incorporated through the availability of related data arising from domains and tasks similar to the one of current interest. Ideally one would like to allow both the data for the current task and for previous related tasks to self-organize the learning system in such a way that commonalities and differences between the tasks are learned in a data-driven fashion. We develop a framework for learning multiple tasks simultaneously, based on sharing features that are common to all tasks, achieved through the use of a modular deep feedforward neural network consisting of shared branches, dealing with the common features of all tasks, and private branches, learning the specific unique aspects of each task. Once an appropriate weight sharing architecture has been established, learning takes place through standard algorithms for feedforward networks, e.g., stochastic gradient descent and its variations. The method deals with meta-learning (such as domain adaptation, transfer and multi-task learning) in a unified fashion, and can easily deal with data arising from different types of sources. Numerical experiments demonstrate the effectiveness of learning in domain adaptation and transfer learning setups, and provide evidence for the flexible and task-oriented representations arising in the network.
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Learning Deep Latent-variable MRFs with Amortized Bethe Free Energy Minimization
MRF latent variable Bethe UGM approximate inference deep generative model
While much recent work has targeted learning deep discrete latent variable models with variational inference, this setting remains challenging, and it is often necessary to make use of potentially high-variance gradient estimators in optimizing the ELBO. As an alternative, we propose to optimize a non-ELBO objective derived from the Bethe free energy approximation to an MRF's partition function. This objective gives rise to a saddle-point learning problem, which we train inference networks to approximately optimize. The derived objective requires no sampling, and can be efficiently computed for many MRFs of interest. We evaluate the proposed approach in learning high-order neural HMMs on text, and find that it often outperforms other approximate inference schemes in terms of true held-out log likelihood. At the same time, we find that all the approximate inference-based approaches to learning high-order neural HMMs we consider underperform learning with exact inference by a significant margin.
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Negative Data Augmentation
generative models self-supervised learning data augmentation anomaly detection
Data augmentation is often used to enlarge datasets with synthetic samples generated in accordance with the underlying data distribution. To enable a wider range of augmentations, we explore negative data augmentation strategies (NDA) that intentionally create out-of-distribution samples. We show that such negative out-of-distribution samples provide information on the support of the data distribution, and can be leveraged for generative modeling and representation learning. We introduce a new GAN training objective where we use NDA as an additional source of synthetic data for the discriminator. We prove that under suitable conditions, optimizing the resulting objective still recovers the true data distribution but can directly bias the generator towards avoiding samples that lack the desired structure. Empirically, models trained with our method achieve improved conditional/unconditional image generation along with improved anomaly detection capabilities. Further, we incorporate the same negative data augmentation strategy in a contrastive learning framework for self-supervised representation learning on images and videos, achieving improved performance on downstream image classification, object detection, and action recognition tasks. These results suggest that prior knowledge on what does not constitute valid data is an effective form of weak supervision across a range of unsupervised learning tasks.
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When Do Curricula Work?
Curriculum Learning Understanding Deep Learning Empirical Investigation
Inspired by human learning, researchers have proposed ordering examples during training based on their difficulty. Both curriculum learning, exposing a network to easier examples early in training, and anti-curriculum learning, showing the most difficult examples first, have been suggested as improvements to the standard i.i.d. training. In this work, we set out to investigate the relative benefits of ordered learning. We first investigate the implicit curricula resulting from architectural and optimization bias and find that samples are learned in a highly consistent order. Next, to quantify the benefit of explicit curricula, we conduct extensive experiments over thousands of orderings spanning three kinds of learning: curriculum, anti-curriculum, and random-curriculum -- in which the size of the training dataset is dynamically increased over time, but the examples are randomly ordered. We find that for standard benchmark datasets, curricula have only marginal benefits, and that randomly ordered samples perform as well or better than curricula and anti-curricula, suggesting that any benefit is entirely due to the dynamic training set size. Inspired by common use cases of curriculum learning in practice, we investigate the role of limited training time budget and noisy data in the success of curriculum learning. Our experiments demonstrate that curriculum, but not anti-curriculum or random ordering can indeed improve the performance either with limited training time budget or in the existence of noisy data.
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Joint Learning of Full-structure Noise in Hierarchical Bayesian Regression Models
Full-structure Noise Hierarchical Bayesian Regression Models Sparse Bayesian Learning Unsupervised Learning Brain Source Imaging Covariance Estimation.
We consider hierarchical Bayesian (type-II maximum likelihood) models for observations with latent variables for source and noise, where both hyperparameters need to be estimated jointly from data. This problem has application in many domains in imaging including biomagnetic inverse problems. Crucial factors influencing accuracy of source estimation are not only the noise level but also its correlation structure, but existing approaches have not addressed estimation of noise covariance matrices with full structure. Here, we consider the reconstruction of brain activity from electroencephalography (EEG). This inverse problem can be formulated as a linear regression with independent Gaussian scale mixture priors for both the source and noise components. As a departure from classical sparse Bayesan learning (SBL) models where across-sensor observations are assumed to be independent and identically distributed, we consider Gaussian noise with full covariance structure. Using Riemannian geometry, we derive an efficient algorithm for updating both source and noise covariance along the manifold of positive definite matrices. Using the majorization-maximization framework, we demonstrate that our algorithm has guaranteed and fast convergence. We validate the algorithm both in simulations and with real data. Our results demonstrate that the novel framework significantly improves upon state-of-the-art techniques in the real-world scenario where the noise is indeed non-diagonal and fully-structured.
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BERT vs ALBERT explained
bert vs albert scale machine learning deep learning models immense amount training time computational resources context language representation learning studies
Implementing Machine Learning and Deep Learning models at scale require an immense amount of training time and computational resources. Particularly in the context of language representation learning, studies have shown that full network pre-training which is large is of crucial importance for achieving state-of-the-art performance. But, we know that increasing the model size results in an increase in the number of model parameters, which significantly increases the training and computation requirements. This can be a huge challenge in the domain of large scale computing. In this blog, we provide a brief summary of the ICLR paper “ALBERT: A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS.” This paper talks about two parameter reduction techniques to lower memory consumption and increase the training speed of the BERT (Bidirectional Encoder Representations from Transformers) architecture. The proposed methods in the paper led to models that scale much better compared to the original BERT.
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Agent S: An Open Agentic Framework that Uses Computers Like a Human
Large Vision and Language Model Agents Retrieval Augmented Generation GUI Large Language Models Agent Computer Interface
We present Agent S, an open agentic framework that enables autonomous interaction with computers through Graphical User Interface (GUI), aimed at transforming human-computer interaction by automating complex, multi-step tasks. Agent S addresses three key challenges in automating computer tasks: acquiring domain-specific knowledge, planning over long task horizons, and handling dynamic, non-uniform interfaces. To this end, Agent S introduces experience-augmented hierarchical planning, which learns from external knowledge search and internal experience retrieval at multiple levels, facilitating efficient task planning and subtask execution. In addition, it employs an Agent-Computer Interface (ACI) to better elicit the reasoning and control capabilities of GUI agents based on Multimodal Large Language Models (MLLMs). Evaluation on the OSWorld benchmark shows that Agent S outperforms the baseline by 9.37% on success rate (an 83.6% relative improvement) and achieves a new state-of-the-art. Comprehensive analysis highlights the effectiveness of individual components and provides insights for future improvements. Furthermore, Agent S demonstrates broad generalizability to different operating systems on a newly-released WindowsAgentArena benchmark. Code will be made publicly available.
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The loss surface and expressivity of deep convolutional neural networks
convolutional neural networks loss surface expressivity critical point global minima linear separability
We analyze the expressiveness and loss surface of practical deep convolutional
neural networks (CNNs) with shared weights and max pooling layers. We show
that such CNNs produce linearly independent features at a “wide” layer which
has more neurons than the number of training samples. This condition holds e.g.
for the VGG network. Furthermore, we provide for such wide CNNs necessary
and sufficient conditions for global minima with zero training error. For the case
where the wide layer is followed by a fully connected layer we show that almost
every critical point of the empirical loss is a global minimum with zero training
error. Our analysis suggests that both depth and width are very important in deep
learning. While depth brings more representational power and allows the network
to learn high level features, width smoothes the optimization landscape of the
loss function in the sense that a sufficiently wide network has a well-behaved loss
surface with almost no bad local minima.
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Training independent subnetworks for robust prediction
Efficient ensembles robustness
Recent approaches to efficiently ensemble neural networks have shown that strong robustness and uncertainty performance can be achieved with a negligible gain in parameters over the original network. However, these methods still require multiple forward passes for prediction, leading to a significant runtime cost. In this work, we show a surprising result:
the benefits of using multiple predictions can be achieved 'for free' under a single model's forward pass. In particular, we show that, using a multi-input multi-output (MIMO) configuration, one can utilize a single model's capacity to train multiple subnetworks that independently learn the task at hand. By ensembling the predictions made by the subnetworks, we improve model robustness without increasing compute. We observe a significant improvement in negative log-likelihood, accuracy, and calibration error on CIFAR10, CIFAR100, ImageNet, and their out-of-distribution variants compared to previous methods.
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Grounding Language to Autonomously-Acquired Skills via Goal Generation
Deep reinforcement learning intrinsic motivations symbolic representations autonomous learning
We are interested in the autonomous acquisition of repertoires of skills. Language-conditioned reinforcement learning (LC-RL) approaches are great tools in this quest, as they allow to express abstract goals as sets of constraints on the states. However, most LC-RL agents are not autonomous and cannot learn without external instructions and feedback. Besides, their direct language condition cannot account for the goal-directed behavior of pre-verbal infants and strongly limits the expression of behavioral diversity for a given language input. To resolve these issues, we propose a new conceptual approach to language-conditioned RL: the Language-Goal-Behavior architecture (LGB). LGB decouples skill learning and language grounding via an intermediate semantic representation of the world. To showcase the properties of LGB, we present a specific implementation called DECSTR. DECSTR is an intrinsically motivated learning agent endowed with an innate semantic representation describing spatial relations between physical objects. In a first stage G -> B, it freely explores its environment and targets self-generated semantic configurations. In a second stage (L -> G), it trains a language-conditioned goal generator to generate semantic goals that match the constraints expressed in language-based inputs. We showcase the additional properties of LGB w.r.t. both an end-to-end LC-RL approach and a similar approach leveraging non-semantic, continuous intermediate representations. Intermediate semantic representations help satisfy language commands in a diversity of ways, enable strategy switching after a failure and facilitate language grounding.
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Improving Generalization with Approximate Factored Value Functions
Generalization Factorization Factored Reward MDP MDP
Reinforcement learning in general unstructured MDPs presents a challenging learning problem. However, certain kinds of MDP structures, such as factorization, are known to make the problem simpler. This fact is often not useful in more complex tasks because complex MDPs with high-dimensional state spaces do not often exhibit such structure, and even if they do, the structure itself is typically unknown. In this work, we instead turn this observation on its head: instead of developing algorithms for structured MDPs, we propose a representation learning algorithm that approximates an unstructured MDP with one that has factorized structure. We then use these factors as a more convenient state representation for downstream learning. The particular structure that we leverage is reward factorization, which defines a more compact class of MDPs that admit factorized value functions. We show that our proposed approach, \textbf{A}pproximately \textbf{Fa}ctored \textbf{R}epresentations (AFaR), can be easily combined with existing RL algorithms, leading to faster training (better sample complexity) and robust zero-shot transfer (better generalization) on the Procgen benchmark. An interesting future work would be to extend AFaR to learn~\textit{factorized} policies that can act on the individual factors that may lead to benefits like better exploration. We empirically verify the effectiveness of our approach in terms of better sample complexity and improved generalization on the ProcGen benchmark and the MiniGrid environments.
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Continual Lifelong Causal Effect Inference with Real World Evidence
continual learning incremental learning causal effect inference representation learning treatment effect estimation
The era of real world evidence has witnessed an increasing availability of observational data, which much facilitates the development of causal effect inference. Although significant advances have been made to overcome the challenges in causal effect estimation, such as missing counterfactual outcomes and selection bias, they only focus on source-specific and stationary observational data. In this paper, we investigate a new research problem of causal effect inference from incrementally available observational data, and present three new evaluation criteria accordingly, including extensibility, adaptability, and accessibility. We propose a Continual Causal Effect Representation Learning method for estimating causal effect with observational data, which are incrementally available from non-stationary data distributions. Instead of having access to all seen observational data, our method only stores a limited subset of feature representations learned from previous data. Combining the selective and balanced representation learning, feature representation distillation, and feature transformation, our method achieves the continual causal effect estimation for new data without compromising the estimation capability for original data. Extensive experiments demonstrate the significance of continual causal effect inference and the effectiveness of our method.
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Multi-Head Attention: Collaborate Instead of Concatenate
attention self-attention bert multi-head tensor factorization
Attention layers are widely used in natural language processing (NLP) and are beginning to influence computer vision architectures. However, they suffer from over-parameterization. For instance, it was shown that the majority of attention heads could be pruned without impacting accuracy. This work aims to enhance current understanding on how multiple heads interact. Motivated by the observation that trained attention heads share common key/query projections, we propose a collaborative multi-head attention layer that enables heads to learn shared projections. Our scheme decreases the number of parameters in an attention layer and can be used as a drop-in replacement in any transformer architecture.For instance, by allowing heads to collaborate on a neural machine translation task, we can reduce the key dimension by 4× without any loss in performance. We also show that it is possible to re-parametrize a pre-trained multi-head attention layer into our collaborative attention layer. Even without retraining, collaborative multi-head attention manages to reduce the size of the key and query projections by half without sacrificing accuracy. Our code is public.
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Knowledge Distillation as Self-Supervised Learning
self-supervised learning knowledge distillation deep learning computer vision
Self-supervised learning (SSL) methods have been shown to effectively train large neural networks with unlabeled data. These networks can produce useful image representations that can exceed the performance of supervised pretraining on downstream tasks. However, SSL is not effective with smaller models. This limits applications where computational power is limited, such as edge devices. Knowledge distillation (KD) is a popular method to train a smaller student network from a larger and more powerful teacher network. The [SEED](https://arxiv.org/abs/2101.04731) paper by Fang et al., published in ICLR 2021, applies knowledge distillation to self-supervised learning to pretrain smaller neural networks without supervision. In this post, we will discuss self-supervised learning and knowledge distillation and how they are unified in SEED.
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Policy Optimization in Zero-Sum Markov Games: Fictitious Self-Play Provably Attains Nash Equilibria
markov games fictitious policy optimization provably fsp smooth fsp opponent ppo nash
Fictitious Self-Play (FSP) has achieved significant empirical success in solving extensive-form games.
However, from a theoretical perspective, it remains unknown whether FSP is guaranteed to converge to Nash equilibria in Markov games.
As an initial attempt, we propose an FSP algorithm for two-player zero-sum Markov games, dubbed as smooth FSP, where both agents adopt an entropy-regularized policy optimization method against each other.
Smooth FSP builds upon a connection between smooth fictitious play and the policy optimization framework. Specifically, in each iteration, each player infers the policy of the opponent implicitly via policy evaluation and improves its current policy by taking the smoothed best-response via a proximal policy optimization (PPO) step.
Moreover, to tame the non-stationarity caused by the opponent, we propose to incorporate entropy regularization in PPO for algorithmic stability.
When both players adopt smooth FSP simultaneously, i.e., with self-play, we prove that the sequence of joint policies converges to a neighborhood of a Nash equilibrium at a sublinear $\tilde{O}(1/T)$ rate, where $T$ is the number of iterations. To our best knowledge, we establish the first finite-time convergence guarantee for FSP-type algorithms in zero-sum Markov games.
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Decoy-enhanced Saliency Maps
Deep neural network Explainable AI Saliency methods Decoys
Saliency methods can make deep neural network predictions more interpretable by identifying a set of critical features in an input sample, such as pixels that contribute most strongly to a prediction made by an image classifier. Unfortunately, recent evidence suggests that many saliency methods poorly perform, especially in situations where gradients are saturated, inputs contain adversarial perturbations, or predictions rely upon inter-feature dependence. To address these issues, we propose a framework that improves the robustness of saliency methods by following a two-step procedure. First, we introduce a perturbation mechanism that subtly varies the input sample without changing its intermediate representations. Using this approach, we can gather a corpus of perturbed data samples while ensuring that the perturbed and original input samples follow the same distribution. Second, we compute saliency maps for the perturbed samples and propose a new method to aggregate saliency maps. With this design, we offset the gradient saturation influence upon interpretation. From a theoretical perspective, we show that the aggregated saliency map not only captures inter-feature dependence but, more importantly, is robust against previously described adversarial perturbation methods. Following our theoretical analysis, we present experimental results suggesting that, both qualitatively and quantitatively, our saliency method outperforms existing methods, in a variety of applications.
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Synthetic Petri Dish: A Novel Surrogate Model for Rapid Architecture Search
Neural Architecture Search AutoML Meta-learning
Neural Architecture Search (NAS) explores a large space of architectural motifs --
a compute-intensive process that often involves ground-truth evaluation of each motif by instantiating it within a large network, and training and evaluating the network with thousands or more data samples. Inspired by how biological motifs such as cells are sometimes extracted from their natural environment and studied in an artificial Petri dish setting, this paper proposes the Synthetic Petri Dish model for evaluating architectural motifs. In the Synthetic Petri Dish, architectural motifs are instantiated in very small networks and evaluated using very few learned synthetic data samples (to effectively approximate performance in the full problem). The relative performance of motifs in the Synthetic Petri Dish can substitute for their ground-truth performance, thus accelerating the most expensive step of NAS. Unlike other neural network-based prediction models that parse the structure of the motif to estimate its performance, the Synthetic Petri Dish predicts motif performance by training the actual motif in an artificial setting, thus deriving predictions from its true intrinsic properties. Experiments in this paper demonstrate that the Synthetic Petri Dish can therefore predict the performance of new motifs with significantly higher accuracy, especially when insufficient ground truth data is available.
Our hope is that this work can inspire a new research direction in studying the performance of extracted components of models in a synthetic diagnostic setting optimized to provide informative evaluations.
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Differentiable Segmentation of Sequences
segmented models segmentation change point detection concept drift warping functions gradient descent
Segmented models are widely used to describe non-stationary sequential data with discrete change points. Their estimation usually requires solving a mixed discrete-continuous optimization problem, where the segmentation is the discrete part and all other model parameters are continuous. A number of estimation algorithms have been developed that are highly specialized for their specific model assumptions. The dependence on non-standard algorithms makes it hard to integrate segmented models in state-of-the-art deep learning architectures that critically depend on gradient-based optimization techniques. In this work, we formulate a relaxed variant of segmented models that enables joint estimation of all model parameters, including the segmentation, with gradient descent. We build on recent advances in learning continuous warping functions and propose a novel family of warping functions based on the two-sided power (TSP) distribution. TSP-based warping functions are differentiable, have simple closed-form expressions, and can represent segmentation functions exactly. Our formulation includes the important class of segmented generalized linear models as a special case, which makes it highly versatile. We use our approach to model the spread of COVID-19 with Poisson regression, apply it on a change point detection task, and learn classification models with concept drift. The experiments show that our approach effectively learns all these tasks with standard algorithms for gradient descent.
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11 Summaries of Papers on Explainable Reinforcement Learning With Some Commentary
interpretability explainability transparency reinforcement learning
Model interpretability was a bullet point in Concrete Problems in AI Safety (2016). Since then, interpretability has come to comprise entire research directions in technical safety agendas (2020). It is safe to say that interpretability is now a very popular area of research. In fact, the topic is sufficiently mainstream that there are books on the topic and corporate services promising to provide it.
Interpretability for reinforcement learning, however, has received much less attention than for supervised learning. So what's the state of research on this topic? What does progress in interpretable RL look like, and are we making progress?
What is this post? This post summarizes 11 recent papers on explaining reinforcement learning agents (in ICLR and related conferences), then provides commentary on the research. The summaries - and not the commentary - are the main point of this post. Though people like paper summaries, this is the kind of interpretive labor that isn't traditionally awarded space in research venues. We primarily select papers appearing between 2018 and 2020, in order to bridge the gap between foundational papers published in 2010-2017 and the more recent and diverse directions of research in the field.
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Does Adam Converge and When?
Adam optimization deep learning
In this blog post, we revisit the (non-)convergence behavior of Adam. Especially, we briefly review the non-convergence results by Reddi et al'19 and the convergence results by Shi et al.'20. Their results take important steps forward to understand Adam better. However, the convergence analysis by Shi et al.'20 requires $\beta_1$ to be either 0 or small enough ($\beta_1$ is the momentum hyperparameter in Adam). Is this a reasonable requirement? If not, how large is the gap between theory and practice? In this blog, we will discuss these questions from multiple different perspectives. We will show that the gap is actually non-negligible, and the discussion on the convergence of Adam is far from being concluded.
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Private Split Inference of Deep Networks
ML privacy split inference
Splitting network computations between the edge device and the cloud server is a promising approach for enabling low edge-compute and private inference of neural networks. Current methods for providing the privacy train the model to minimize information leakage for a given set of private attributes. In practice, however, the test queries might contain private attributes that are not foreseen during training.
We propose an alternative solution, in which, instead of obfuscating the information corresponding to a set of attributes, the edge device discards the information irrelevant to the main task. To this end, the edge device runs the model up to a split layer determined based on its computational capacity and then removes the activation content that is in the null space of the next layer of the model before sending it to the server. It can further remove the low-energy components of the remaining signal to improve the privacy at the cost of reducing the accuracy. The experimental results show that our methods provide privacy while maintaining the accuracy and introducing only a small computational overhead.
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A Truly Constant-time Distribution-aware Negative Sampling
truly approaches negative classes distribution sampling scheme adaptive constant time iteration negative
Softmax classifiers with a very large number of classes naturally occur in many applications such as natural language processing and information retrieval. The calculation of full-softmax is very expensive from the computational and energy perspective. There have been a variety of sampling approaches to overcome this challenge, popularly known as negative sampling (NS). Ideally, NS should sample negative classes from a distribution that is dependent on the input data, the current parameters, and the correct positive class. Unfortunately, due to the dynamically updated parameters and data samples, there does not exist any sampling scheme that is truly adaptive and also samples the negative classes in constant time every iteration. Therefore, alternative heuristics like random sampling, static frequency-based sampling, or learning-based biased sampling; which primarily trade either the sampling cost or the adaptivity of samples per iteration, are adopted. In this paper, we show a class of distribution where the sampling scheme is truly adaptive and provably generates negative samples in constant time. We demonstrate a negative sampling implementation that is significantly faster, in terms of wall clock time, compared to the most optimized TensorFlow implementations of standard softmax or other sampling approaches on the best available GPUs (V100s).
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Recall Loss for Imbalanced Image Classification and Semantic Segmentation
Data Imbalance Classification Semantic Segmentation Deep Learning
Class imbalance is a fundamental problem in computer vision applications such as semantic segmentation and image classification. Specifically, uneven class distributions in a training dataset often result in unsatisfactory performance on under-represented classes. Many works have proposed to weigh the standard cross entropy loss function with pre-computed weights based on class statistics such as the number of samples and class margins. There are two major drawbacks to these methods: 1) constantly up-weighing minority classes can introduce excessive false positives especially in semantic segmentation; 2) many recent works discovered that pre-computed weights have adversarial effects on representation learning. In this regard, we propose a hard-class mining loss by reshaping the vanilla cross entropy loss such that it weights the loss for each class dynamically based on changing recall performance. We show mathematically that the novel recall loss changes gradually between the standard cross entropy loss and the well-known inverse frequency cross entropy loss and balances precision and accuracy. We first demonstrate that the proposed loss effectively balances precision and accuracy on semantic segmentation datasets, and leads to significant performance improvement over other state-of-the-art loss functions used in semantic segmentation, especially on shallow networks. On image classification, we design a simple two-head training strategy to show that the novel loss function improves representation learning on imbalanced datasets. We outperform the previously best performing method by $5.7\%$ on Place365-LT and by $1.1\%$ on iNaturalist.
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Deep Q Learning from Dynamic Demonstration with Behavioral Cloning
deep q dynamic demonstration behavioral drl drl model model deep reinforcement learning capability optimal policies simulation environments
Although Deep Reinforcement Learning (DRL) has proven its capability to learn optimal policies by directly interacting with simulation environments, how to combine DRL with supervised learning and leverage additional knowledge to assist the DRL agent effectively still remains difficult. This study proposes a novel approach integrating deep Q learning from dynamic demonstrations with a behavioral cloning model (DQfDD-BC), which includes a supervised learning technique of instructing a DRL model to enhance its performance. Specifically, the DQfDD-BC model leverages historical demonstrations to pre-train a supervised BC model and consistently update it by learning the dynamically updated demonstrations. Then the DQfDD-BC model manages the sample complexity by exploiting both the historical and generated demonstrations. An expert loss function is designed to compare actions generated by the DRL model with those obtained from the BC model to provide advantageous guidance for policy improvements. Experimental results in several OpenAI Gym environments show that the proposed approach adapts to different performance levels of demonstrations, and meanwhile, accelerates the learning processes. As illustrated in an ablation study, the dynamic demonstration and expert loss mechanisms with the utilization of a BC model contribute to improving the learning convergence performance compared with the origin DQfD model.
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Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models
generative models classifiers adversarial attacks recent years neural network approaches machine learning tasks classification vulnerable adversarial perturbations small perturbations
In recent years, deep neural network approaches have been widely adopted for machine learning tasks, including classification. However, they were shown to be vulnerable to adversarial perturbations: carefully crafted small perturbations can cause misclassification of legitimate images. We propose Defense-GAN, a new framework leveraging the expressive capability of generative models to defend deep neural networks against such attacks. Defense-GAN is trained to model the distribution of unperturbed images. At inference time, it finds a close output to a given image which does not contain the adversarial changes. This output is then fed to the classifier. Our proposed method can be used with any classification model and does not modify the classifier structure or training procedure. It can also be used as a defense against any attack as it does not assume knowledge of the process for generating the adversarial examples. We empirically show that Defense-GAN is consistently effective against different attack methods and improves on existing defense strategies.
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On the Explicit Role of Initialization on the Convergence and Generalization Properties of Overparametrized Linear Networks
initialization random initialization explicit role convergence generalization properties generalization performance optimization imbalance manifold solution
Neural networks trained via gradient descent with random initialization and without any regularization enjoy good generalization performance in practice despite being highly overparametrized. A promising direction to explain this phenomenon is the \emph{Neural Tangent Kernel} (NTK), which characterizes the implicit regularization effect of gradient flow/descent on infinitely wide neural networks with random initialization. However, a non-asymptotic analysis that connects generalization performance, initialization, and optimization for finite width networks remains elusive. In this paper, we present a novel analysis of overparametrized single-hidden layer linear networks, which formally connects initialization, optimization, and overparametrization with generalization performance. We exploit the fact that gradient flow preserves a certain matrix that characterizes the \emph{imbalance} of the network weights, to show that the squared loss converges exponentially at a rate that depends on the level of imbalance of the initialization. Such guarantees on the convergence rate allow us to show that large hidden layer width, together with (properly scaled) random initialization, implicitly constrains the dynamics of the network parameters to be close to a low-dimensional manifold. In turn, minimizing the loss over this manifold leads to solutions with good generalization, which correspond to the min-norm solution in the linear case. Finally, we derive a novel $\mathcal{O}( h^{-1/2})$ upper-bound on the operator norm distance between the trained network and the min-norm solution, where $h$ is the hidden layer width.
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Unsupervised Progressive Learning and the STAM Architecture
continual learning unsupervised learning representation learning online learning
We first pose the Unsupervised Progressive Learning (UPL) problem: an online representation learning problem in which the learner observes a non-stationary and unlabeled data stream, and identifies a growing number of features that persist over time even though the data is not stored or replayed. To solve the UPL problem we propose the Self-Taught Associative Memory (STAM) architecture. Layered hierarchies of STAM modules learn based on a combination of online clustering, novelty detection, forgetting outliers, and storing only prototypical features rather than specific examples. We evaluate STAM representations using classification and clustering tasks. While there are no existing learning scenarios which are directly comparable to UPL, we compare the STAM architecture with two recent continual learning works; Memory Aware Synapses (MAS), and Gradient Episodic Memories (GEM), which have been modified to be suitable for the UPL setting.
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Identifying Analogies Across Domains
unsupervised mapping cross domain mapping
Identifying analogies across domains without supervision is a key task for artificial intelligence. Recent advances in cross domain image mapping have concentrated on translating images across domains. Although the progress made is impressive, the visual fidelity many times does not suffice for identifying the matching sample from the other domain. In this paper, we tackle this very task of finding exact analogies between datasets i.e. for every image from domain A find an analogous image in domain B. We present a matching-by-synthesis approach: AN-GAN, and show that it outperforms current techniques. We further show that the cross-domain mapping task can be broken into two parts: domain alignment and learning the mapping function. The tasks can be iteratively solved, and as the alignment is improved, the unsupervised translation function reaches quality comparable to full supervision.
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Pre-training Text-to-Text Transformers for Concept-centric Common Sense
Language Model Pre-training Commonsense Reasoning Self-supervised Learning
Pretrained language models (PTLM) have achieved impressive results in a range of natural language understanding (NLU) and generation (NLG) tasks that require a syntactic and semantic understanding of the text. However, current pre-training objectives such as masked token prediction (for BERT-style PTLMs) and masked span infilling (for T5-style PTLMs) do not explicitly model the relational and compositional commonsense knowledge about everyday concepts, which is crucial to many downstream tasks requiring commonsense reasoning. To augment PTLMs with common sense, we propose generative and contrastive objectives as intermediate self-supervised pre-training tasks between general pre-training and downstream task-specific fine-tuning. We also propose a joint training framework to unify generative and contrastive objectives so that these objectives can be more effective.
Our proposed objectives can pack more commonsense knowledge into the parameters of a pre-trained text-to-text transformer without relying on external knowledge bases, yielding better performance on both NLU and NLG tasks. We apply our method on a pre-trained T5 model in an intermediate task transfer learning fashion to train a concept-aware language model (CALM) and experiment with five commonsense benchmarks (four NLU tasks and one NLG task). Experimental results show that CALM outperforms baseline methods by a consistent margin.
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New Bounds For Distributed Mean Estimation and Variance Reduction
distributed machine learning mean estimation variance reduction lattices
We consider the problem of distributed mean estimation (DME), in which $n$ machines are each given a local $d$-dimensional vector $\mathbf x_v \in \mathbb R^d$, and must cooperate to estimate the mean of their inputs $\mathbf \mu = \frac 1n\sum_{v = 1}^n \mathbf x_v$, while minimizing total communication cost. DME is a fundamental construct in distributed machine learning, and there has been considerable work on variants of this problem, especially in the context of distributed variance reduction for stochastic gradients in parallel SGD. Previous work typically assumes an upper bound on the norm of the input vectors, and achieves an error bound in terms of this norm. However, in many real applications, the input vectors are concentrated around the correct output $\mathbf \mu$, but $\mathbf \mu$ itself has large norm. In such cases, previous output error bounds perform poorly.
In this paper, we show that output error bounds need not depend on input norm. We provide a method of quantization which allows distributed mean estimation to be performed with solution quality dependent only on the distance between inputs, not on input norm, and show an analogous result for distributed variance reduction. The technique is based on a new connection with lattice theory. We also provide lower bounds showing that the communication to error trade-off of our algorithms is asymptotically optimal. As the lattices achieving optimal bounds under $\ell_2$-norm can be computationally impractical, we also present an extension which leverages easy-to-use cubic lattices, and is loose only up to a logarithmic factor in $d$. We show experimentally that our method yields practical improvements for common applications, relative to prior approaches.
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Neural Lyapunov Model Predictive Control
optimal control mpc lyapunov neural networks safe-learning safety
With a growing interest in data-driven control techniques, Model Predictive Control (MPC) provides a significant opportunity to exploit the surplus of data reliably, particularly while taking safety and stability into account. In this paper, we aim to infer the terminal cost of an MPC controller from transitions generated by an initial \emph{unknown} demonstrator. We propose an algorithm to alternatively learn the terminal cost and update the MPC parameters according to a stability metric. We design the terminal cost as a Lyapunov function neural network and theoretically show that, under limited approximation error, our proposed approach guarantees that the size of the stability region (region of attraction) is greater than or equal to the one from the initial demonstrator. We also present theorems that characterize the stability and performance of the learned MPC in the presence of model uncertainties and sub-optimality due to function approximation. Empirically, we demonstrate the efficacy of the proposed algorithm on non-linear continuous control tasks with soft constraints. Our results show that the proposed approach can improve upon the initial demonstrator also in practice and achieve better task performance than other learning-based baselines.
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Semantic Re-tuning with Contrastive Tension
Semantic Textual Similarity Transformers Language Modelling Sentence Embeddings Sentence Representations Pre-training Fine-tuning
Extracting semantically useful natural language sentence representations from pre-trained deep neural networks such as Transformers remains a challenge. We first demonstrate that pre-training objectives impose a significant task bias onto the final layers of models with a layer-wise survey of the Semantic Textual Similarity (STS) correlations for multiple common Transformer language models. We then propose a new self-supervised method called Contrastive Tension (CT) to counter such biases. CT frames the training objective as a noise-contrastive task between the final layer representations of two independent models, in turn making the final layer representations suitable for feature extraction. Results from multiple common unsupervised and supervised STS tasks indicate that CT outperforms previous State Of The Art (SOTA), and when combining CT with supervised data we improve upon previous SOTA results with large margins.
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Don't be picky, all students in the right family can learn from good teachers
knowledge distillation neural architecture search nas automl knowledge trasfer model compression
State-of-the-art results in deep learning have been improving steadily, in good part due to the use of larger models. However, widespread use is constrained by device hardware limitations, resulting in a substantial performance gap between state-of-the-art models and those that can be effectively deployed on small devices.
While Knowledge Distillation (KD) theoretically enables small student models to emulate larger teacher models, in practice selecting a good student architecture requires considerable human expertise. Neural Architecture Search (NAS) appears as a natural solution to this problem but most approaches can be inefficient, as most of the computation is spent comparing architectures sampled from the same distribution, with negligible differences in performance.
In this paper, we propose to instead search for a family of student architectures sharing the property of being good at learning from a given teacher.
Our approach AutoKD, powered by Bayesian Optimization, explores a flexible graph-based search space, enabling us to automatically learn the optimal student architecture distribution and KD parameters, while being 20x more sample efficient compared to existing state-of-the-art. We evaluate our method on 3 datasets; on large images specifically, we reach the teacher performance while using 3x less memory and 10x less parameters. Finally, while AutoKD uses the traditional KD loss, it outperforms more advanced KD variants using hand-designed students.
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Certified robustness against physically-realizable patch attack via randomized cropping
adversarial machine learning certifiable defense patch attack
This paper studies a certifiable defense against adversarial patch attacks on image classification. Our approach classifies random crops from the original image independently and the original image is classified as the vote over these crops. This process minimizes changes to the training process, as only the crop classification model needs to be trained, and can be trained in a standard manner without explicit adversarial training. Leveraging the fact that a patch attack can only influence some pixels of the image, we derive certified robustness bounds on the resulting classification. Our method is particularly effective when realistic physical transformations are applied to the adversarial patch, such as affine transformations. Such transformations occur naturally when an adversarial patch is physically introduced to a scene. Our method improves upon the current state of the art in defending against patch attacks on CIFAR10 and ImageNet, both in terms of certified accuracy and inference time.
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Sandwich Batch Normalization
normalization
We present Sandwich Batch Normalization ($\textbf{SaBN}$), a frustratingly easy improvement of Batch Normalization (BN) with only a few lines of code changes. SaBN is motivated by addressing the inherent $\textit{feature distribution heterogeneity}$ that one can be identified in many tasks, which can arise from model heterogeneity (dynamic architectures, model conditioning, etc.), or data heterogeneity (multiple input domains). A SaBN factorizes the BN affine layer into one shared $\textit{sandwich affine}$ layer, cascaded by several parallel $\textit{independent affine}$ layers. Its variants include further decomposing the normalization layer into multiple parallel ones, and extending similar ideas to instance normalization. We demonstrate the prevailing effectiveness of SaBN (as well as its variants) as a $\textbf{drop-in replacement in four tasks}$: neural architecture search (NAS), image generation, adversarial training, and style transfer. Leveraging SaBN immediately boosts two state-of-the-art weight-sharing NAS algorithms significantly on NAS-Bench-201; achieves better Inception Score and FID on CIFAR-10 and ImageNet conditional image generation with three state-of-the art GANs; substantially improves the robust and standard accuracy for adversarial defense; and produces superior arbitrary stylized results. We also provide visualizations and analysis to help understand why SaBN works. All our codes and pre-trained models will be released upon acceptance.
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Towards Self-Supervised Learning of Global and Object-Centric Representations
self-supervised learning object representations contrastive loss slot attention vision transformer CLEVR
Self-supervision allows learning meaningful representations of natural images, which usually contain one central object. How well does it transfer to multi-entity scenes? We discuss key aspects of learning structured object-centric representations with self-supervision and validate our insights through several experiments on the CLEVR dataset. Regarding the architecture, we confirm the importance of competition for attention-based object discovery, where each image patch is exclusively attended by one object. For training, we show that contrastive losses equipped with matching can be applied directly in a latent space, avoiding pixel-based reconstruction. However, such an optimization objective is sensitive to false negatives (recurring objects) and false positives (matching errors). Careful consideration is thus required around data augmentation and negative sample selection.
Code, datasets, and notebooks are available at https://github.com/baldassarreFe/iclr-osc-22.
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TextTN: Probabilistic Encoding of Language on Tensor Network
Tensor Network Language Representation Natural Language Processing Quantum Machine Learning Entanglement Entropy
As a novel model that bridges machine learning and quantum theory, tensor network (TN) has recently gained increasing attention and successful applications for processing natural images. However, for natural languages, it is unclear how to design a probabilistic encoding architecture to efficiently and accurately learn and classify texts based on TN. This paper proposes a general two-step scheme of text classification based on Tensor Network, which is named as TextTN. TextTN first encodes the word vectors in a probabilistic space by a generative TN (word-GTN), and then classifies a text sentence using a discriminative TN (sentence-DTN). Moreover, in sentence-DTN, its hyper-parameter (i.e., bond-dimension) can be analyzed and selected by the theoretical property of TextTN's expressive power. In experiments, our TextTN also obtains the state-of-the-art result on SST-5 sentiment classification task.
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XLVIN: eXecuted Latent Value Iteration Nets
value iteration graph neural networks reinforcement learning
Value Iteration Networks (VINs) have emerged as a popular method to perform implicit planning within deep reinforcement learning, enabling performance improvements on tasks requiring long-range reasoning and understanding of environment dynamics. This came with several limitations, however: the model is not explicitly incentivised to perform meaningful planning computations, the underlying state space is assumed to be discrete, and the Markov decision process (MDP) is assumed fixed and known. We propose eXecuted Latent Value Iteration Networks (XLVINs), which combine recent developments across contrastive self-supervised learning, graph representation learning and neural algorithmic reasoning to alleviate all of the above limitations, successfully deploying VIN-style models on generic environments. XLVINs match the performance of VIN-like models when the underlying MDP is discrete, fixed and known, and provide significant improvements to model-free baselines across three general MDP setups.
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