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Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections
time series sequential data representation learning low-rank tensors classification generative modelling
Sequential data such as time series, video, or text can be challenging to analyse as the ordered structure gives rise to complex dependencies. At the heart of this is non-commutativity, in the sense that reordering the elements of a sequence can completely change its meaning. We use a classical mathematical object -- the free algebra -- to capture this non-commutativity. To address the innate computational complexity of this algebra, we use compositions of low-rank tensor projections. This yields modular and scalable building blocks that give state-of-the-art performance on standard benchmarks such as multivariate time series classification, mortality prediction and generative models for video.
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Online Meta-Learning
meta learning few-shot learning online learning
A central capability of intelligent systems is the ability to continuously build upon previous experiences to speed up and enhance learning of new tasks. Two distinct research paradigms have studied this question. Meta-learning views this problem as learning a prior over model parameters that is amenable for fast adaptation on a new task, but typically assumes the set of tasks are available together as a batch. In contrast, online (regret based) learning considers a sequential setting in which problems are revealed one after the other, but conventionally train only a single model without any task-specific adaptation. This work introduces an online meta-learning setting, which merges ideas from both the aforementioned paradigms to better capture the spirit and practice of continual lifelong learning. We propose the follow the meta leader (FTML) algorithm which extends the MAML algorithm to this setting. Theoretically, this work provides an O(logT) regret guarantee for the FTML algorithm. Our experimental evaluation on three different large-scale tasks suggest that the proposed algorithm significantly outperforms alternatives based on traditional online learning approaches.
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The Steganographic Potentials of Language Models
Computer Science - Computation and Language Computer Science - Cryptography and Security Computer Science - Machine Learning
The potential for large language models (LLMs) to hide messages within plain text (steganography) poses a challenge to detection and thwarting of unaligned AI agents, and undermines faithfulness of LLMs reasoning. We explore the steganographic capabilities of LLMs fine-tuned via reinforcement learning (RL) to: (1) develop covert encoding schemes, (2) engage in steganography when prompted, and (3) utilize steganography in realistic scenarios where hidden reasoning is likely, but not prompted. In these scenarios, we detect the intention of LLMs to hide their reasoning as well as their steganography performance. Our findings in the fine-tuning experiments as well as in behavioral non fine-tuning evaluations reveal that while current models exhibit rudimentary steganographic abilities in terms of security and capacity, explicit algorithmic guidance markedly enhances their capacity for information concealment.
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Episodic Memory for Learning Subjective-Timescale Models
Episodic Memory Time Perception Active Inference Model-based Reinforcement Learning
In model-based learning, an agent’s model is commonly defined over transitions between consecutive states of an environment even though planning often requires reasoning over multi-step timescales, with intermediate states either unnecessary, or worse, accumulating prediction error. In contrast, intelligent behaviour in biological organisms is characterised by the ability to plan over varying temporal scales depending on the context. Inspired by the recent works on human time perception, we devise a novel approach to learning a transition dynamics model, based on the sequences of episodic memories that define the agent's subjective timescale – over which it learns world dynamics and over which future planning is performed. We implement this in the framework of active inference and demonstrate that the resulting subjective-timescale model (STM) can systematically vary the temporal extent of its predictions while preserving the same computational efficiency. Additionally, we show that STM predictions are more likely to introduce future salient events (for example new objects coming into view), incentivising exploration of new areas of the environment. As a result, STM produces more informative action-conditioned roll-outs that assist the agent in making better decisions. We validate significant improvement in our STM agent's performance in the Animal-AI environment against a baseline system, trained using the environment's objective-timescale dynamics.
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Comparing PINNs Across Frameworks: JAX, TensorFlow, and PyTorch
Physics-informed neural Networks Compilers XLA JAX TensorFlow PyTorch
Physics-Informed Neural Networks (PINNs) have become a pivotal technology for adhering to physical laws and solving nonlinear partial differential equations (PDEs). Enhancing the performance of PINN implementations can significantly quicken the pace of simulations and foster the creation of innovative methodologies. This paper presents `PINNs-JAX', an innovative implementation that utilizes the JAX framework to leverage the distinct capabilities of XLA compilers. This approach aims to improve computational efficiency and flexibility within PINN applications. We conduct a comprehensive comparison of PINNs-JAX against traditional PINN implementations in widely-used frameworks such as TensorFlow V1, TensorFlow V2, and PyTorch, evaluating performance across a variety of six different examples. These include continuous, discrete, forward, and inverse problems. Our findings indicate that PINNs implemented with JAX outperform in simpler examples, yet TensorFlow V2 presents potential benefits for tackling large-scale challenges, as exemplified by the 3D-Navier Stokes case. To support collaborative development and further research, we have made the source code available to the public at: https://github.com/rezaakb/pinns-jax.
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Neural Clustering By Predicting And Copying Noise
unsupervised learning clustering deep learning
We propose a neural clustering model that jointly learns both latent features and how they cluster. Unlike similar methods our model does not require a predefined number of clusters. Using a supervised approach, we agglomerate latent features towards randomly sampled targets within the same space whilst progressively removing the targets until we are left with only targets which represent cluster centroids. To show the behavior of our model across different modalities we apply our model on both text and image data and very competitive results on MNIST. Finally, we also provide results against baseline models for fashion-MNIST, the 20 newsgroups dataset, and a Twitter dataset we ourselves create.
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Fast Proxies for LLM Robustness Evaluation
LLM Robustness Red-Teaming
Evaluating the robustness of LLMs to adversarial attacks is crucial for safe deployment, yet current red-teaming methods are often prohibitively expensive.
We compare the ability of fast proxy metrics to predict the real-world robustness of an LLM against a simulated attacker ensemble.
This allows us to estimate a model's robustness to computationally expensive attacks without requiring runs of the attacks themselves.
Specifically, we consider gradient-descent-based embedding-space attacks, prefilling attacks, and direct attacks.
Even though direct attacks in particular do not achieve high ASR, we find that they and embedding-space attacks can predict attack success rates well, achieving $r_p=0.86$ (linear) and $r_s=0.97$ (Spearman rank) correlations with the full attack ensemble while reducing computational cost by three orders of magnitude.
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Token-Level Adversarial Prompt Detection Based on Perplexity Measures and Contextual Information
adversarial prompt detection llm security
In recent years, Large Language Models (LLMs) have emerged as pivotal tools in various applications. However, these models are susceptible to adversarial prompt attacks, where attackers can carefully curate input strings that mislead LLMs into generating incorrect or undesired outputs. Previous work has revealed that with relatively simple yet effective attacks based on discrete optimization, it is possible to generate adversarial prompts that bypass moderation and alignment of the models. This vulnerability to adversarial prompts underscores a significant concern regarding the robustness and reliability of LLMs. Our work aims to address this concern by introducing a novel approach to detecting adversarial prompts at a token level, leveraging the LLM's capability to predict the next token's probability. We measure the degree of the model's perplexity, where tokens predicted with high probability are considered normal, and those exhibiting high perplexity are flagged as adversarial. Additionaly, our method also integrates context understanding by incorporating neighboring token information to encourage the detection of contiguous adversarial prompt sequences. To this end, we design two algorithms for adversarial prompt detection: one based on optimization techniques and another on Probabilistic Graphical Models (PGM). Both methods are equipped with efficient solving methods, ensuring efficient adversarial prompt detection. Our token-level detection result can be visualized as heatmap overlays on the text sequence, allowing for a clearer and more intuitive representation of which part of the text may contain adversarial prompts.
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ON NEURAL NETWORK GENERALIZATION VIA PROMOTING WITHIN-LAYER ACTIVATION DIVERSITY
Deep learning
During the last decade, neural networks have been intensively used to tackle various problems and they have often led to state-of-the-art results. These networks are composed of multiple jointly optimized layers arranged in a hierarchical structure. At each layer, the aim is to learn to extract hidden patterns needed to solve the problem at hand and forward it to the next layers. In the standard form, a neural network is trained with gradient-based optimization, where the errors are back-propagated from the last layer back to the first one. Thus at each optimization step, neurons at a given layer receive feedback from neurons belonging to higher layers of the hierarchy. In this paper, we propose to complement this traditional 'between-layer' feedback with additional 'within-layer' feedback to encourage diversity of the activations within the same layer. To this end, we measure the pairwise similarity between the outputs of the neurons and use it to model the layer's overall diversity. By penalizing similarities and promoting diversity, we encourage each neuron to learn a distinctive representation and, thus, to enrich the data representation learned within the layer and to increase the total capacity of the model. We theoretically study how the within-layer activation diversity affects the generalization performance of a neural network in a supervised context and we prove that increasing the diversity of hidden activations reduces the estimation error. In addition to the theoretical guarantees, we present an empirical study confirming that the proposed approach enhances the performance of neural networks.
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Probabilistic Numeric Convolutional Neural Networks
probabilistic numerics gaussian processes discretization error pde superpixel irregularly spaced time series misssing data spatial uncertainty
Continuous input signals like images and time series that are irregularly sampled or have missing values are challenging for existing deep learning methods. Coherently defined feature representations must depend on the values in unobserved regions of the input. Drawing from the work in probabilistic numerics, we propose Probabilistic Numeric Convolutional Neural Networks which represent features as Gaussian processes, providing a probabilistic description of discretization error. We then define a convolutional layer as the evolution of a PDE defined on this GP, followed by a nonlinearity. This approach also naturally admits steerable equivariant convolutions under e.g. the rotation group. In experiments we show that our approach yields a $3\times$ reduction of error from the previous state of the art on the SuperPixel-MNIST dataset and competitive performance on the medical time series dataset PhysioNet2012.
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Semi-Discrete Normalizing Flows through Differentiable Voronoi Tessellation
normalizing discrete differentiable voronoi tessellation continuous distributions difficult task many heuristical approaches quantization boundaries continuous space
Mapping between discrete and continuous distributions is a difficult task and many have had to resort to approximate or heuristical approaches. We propose a tessellation-based approach that directly learns quantization boundaries on a continuous space, complete with exact likelihood evaluations. This is done through constructing normalizing flows on convex polytopes defined via a differentiable tessellation. Using a simple homeomorphism with an efficient log determinant Jacobian, we can then cheaply parameterize distributions on bounded domains. We explore this approach in two application settings, mapping from discrete to continuous and vice versa. Firstly, a Voronoi dequantization allows automatically learning quantization boundaries in a multidimensional space. The location of boundaries and distances between regions can encode useful structural relations between the quantized discrete values. Secondly, a Voronoi mixture model has constant computation cost for likelihood evaluation regardless of the number of mixture components. Empirically, we show improvements over existing methods across a range of structured data modalities.
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Distributed Associative Memory Network with Association Reinforcing Loss
memory augmented neural network distributed memory memorization relational reasoning
Despite recent progress in memory augmented neural network research, associative memory networks with a single external memory still show limited performance on complex relational reasoning tasks. The main reason for this problem comes from the lossy representation of a content-based addressing memory and its insufficient associating performance for long temporal sequence data. To address these problems, here we introduce a novel Distributed Associative Memory architecture (DAM) with Association Reinforcing Loss (ARL) function which enhances the relation reasoning performance of memory augmented neural network. In this framework, instead of relying on a single large external memory, we form a set of multiple smaller associative memory blocks and update these sub-memory blocks simultaneously and independently with the content-based addressing mechanism. Based on DAM architecture, we can effectively retrieve complex relational information by integrating diverse representations distributed across multiple sub-memory blocks with an attention mechanism. Moreover, to further enhance the relation modeling performance of memory network, we propose ARL which assists a task's target objective while learning relational information exist in data. ARL enables the memory augmented neural network to reinforce an association between input data and task objective by reproducing stochastically sampled input data from stored memory contents. With this content reproducing task, it enriches the representations with relational information. In experiments, we apply our two main approaches to Differential Neural Computer (DNC), which is one of the representative content-based addressing memory model and achieves state-of-the-art performance on both memorization and relational reasoning tasks.
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Towards Robust Neural Networks via Close-loop Control
neural network robustness optimal control dynamical system
Despite their success in massive engineering applications, deep neural networks are vulnerable to various perturbations due to their black-box nature. Recent study has shown that a deep neural network can misclassify the data even if the input data is perturbed by an imperceptible amount. In this paper, we address the robustness issue of neural networks by a novel close-loop control method from the perspective of dynamic systems. Instead of modifying the parameters in a fixed neural network architecture, a close-loop control process is added to generate control signals adaptively for the perturbed or corrupted data. We connect the robustness of neural networks with optimal control using the geometrical information of underlying data to design the control objective. The detailed analysis shows how the embedding manifolds of state trajectory affect error estimation of the proposed method. Our approach can simultaneously maintain the performance on clean data and improve the robustness against many types of data perturbations. It can also further improve the performance of robustly trained neural networks against different perturbations. To the best of our knowledge, this is the first work that improves the robustness of neural networks with close-loop control.
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Unsupervised Scalable Representation Learning for Multivariate Time Series
time series representation learning unsupervised learning
Time series constitute a challenging data type for machine learning algorithms, due to their highly variable lengths and sparse labeling in practice. In this paper, we tackle this challenge by proposing an unsupervised method to learn universal embeddings of time series. Unlike previous works, it is scalable with respect to their length and we demonstrate the quality, transferability and practicability of the learned representations with thorough experiments and comparisons. To this end, we combine an encoder based on causal dilated convolutions with a novel triplet loss employing time-based negative sampling, obtaining general-purpose representations for variable length and multivariate time series.
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On Self-Supervised Image Representations for GAN Evaluation
GAN evaluation embedding
The embeddings from CNNs pretrained on Imagenet classification are de-facto standard image representations for assessing GANs via FID, Precision and Recall measures. Despite broad previous criticism of their usage for non-Imagenet domains, these embeddings are still the top choice in most of the GAN literature.
In this paper, we advocate the usage of the state-of-the-art self-supervised representations to evaluate GANs on the established non-Imagenet benchmarks. These representations, typically obtained via contrastive learning, are shown to provide better transfer to new tasks and domains, therefore, can serve as more universal embeddings of natural images. With extensive comparison of the recent GANs on the common datasets, we show that self-supervised representations produce a more reasonable ranking of models in terms of FID/Precision/Recall, while the ranking with classification-pretrained embeddings often can be misleading.
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Towards Finding Longer Proofs
automated reasoning reinforcement learning reasoning by analogy
We present a reinforcement learning (RL) based guidance system for automated theorem proving geared towards Finding Longer Proofs (FLoP). FLoP is a step towards learning to reason by analogy, reducing the dependence on large scale search in automated theorem provers. We use several simple, structured datasets with very long proofs to show that FLoP can successfully generalise a single training proof to a large class of related problems, implementing a simple form of analogical reasoning. On these benchmarks, FLoP is competitive with strong theorem provers despite using very limited search.
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Understanding and Improving Lexical Choice in Non-Autoregressive Translation
lexical choice raw data words models lexical choice errors nat model understanding translation understanding translation knowledge distillation
Knowledge distillation (KD) is essential for training non-autoregressive translation (NAT) models by reducing the complexity of the raw data with an autoregressive teacher model. In this study, we empirically show that as a side effect of this training, the lexical choice errors on low-frequency words are propagated to the NAT model from the teacher model. To alleviate this problem, we propose to expose the raw data to NAT models to restore the useful information of low-frequency words, which are missed in the distilled data. To this end, we introduce an extra Kullback-Leibler divergence term derived by comparing the lexical choice of NAT model and that embedded in the raw data. Experimental results across language pairs and model architectures demonstrate the effectiveness and universality of the proposed approach. Extensive analyses confirm our claim that our approach improves performance by reducing the lexical choice errors on low-frequency words. Encouragingly, our approach pushes the SOTA NAT performance on the WMT14 English-German and WMT16 Romanian-English datasets up to 27.8 and 33.8 BLEU points, respectively.
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Unpacking Information Bottlenecks: Surrogate Objectives for Deep Learning
deep learning information bottleneck information theory
The Information Bottleneck principle offers both a mechanism to explain how deep neural networks train and generalize, as well as a regularized objective with which to train models. However, multiple competing objectives are proposed in the literature, and the information-theoretic quantities used in these objectives are difficult to compute for large deep neural networks, which in turn limits their use as a training objective. In this work, we review these quantities, compare and unify previously proposed objectives, which allows us to develop surrogate objectives more friendly to optimization without relying on cumbersome tools such as density estimation. We find that these surrogate objectives allow us to apply the information bottleneck to modern neural network architectures. We demonstrate our insights on MNIST, CIFAR-10 and ImageNette with modern DNN architectures (ResNets).
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DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION
Transformer Attention Natural Language Processing Language Model Pre-training Position Encoding
Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks. In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT with disentangled attention) that improves the BERT and RoBERTa models using two novel techniques. The first is the disentangled attention mechanism, where each word is represented using two vectors that encode its content and position, respectively, and the attention weights among words are computed using disentangled matrices on their contents and relative positions, respectively. Second, an enhanced mask decoder is used to incorporate absolute positions in the decoding layer to predict the masked tokens in model pre-training. In addition, a new virtual adversarial training method is used for fine-tuning to improve models’
generalization. We show that these techniques significantly improve the efficiency of model pre-training and the performance of both natural language understand(NLU) and natural langauge generation (NLG) downstream tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half of the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9% (90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%). Notably, we scale up DeBERTa by training a larger version that consists of 48 Transform layers with 1.5 billion parameters. The significant performance boost makes the single DeBERTa model surpass the human performance on the SuperGLUE benchmark (Wang et al., 2019a) for the first time in terms of macro-average score (89.9 versus 89.8), and the ensemble DeBERTa model sits atop the SuperGLUE leaderboard as of January 6, 2021, outperforming the human baseline by a decent margin (90.3 versus
89.8). The pre-trained DeBERTa models and the source code were released at: https://github.com/microsoft/DeBERTa.
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Deep Ensemble Kernel Learning
kernel-learning gaussian-process Bayesian ensemble
Gaussian processes (GPs) are nonparametric Bayesian models that are both flexible and robust to overfitting. One of the main challenges of GP methods is selecting the kernel. In the deep kernel learning (DKL) paradigm, a deep neural network or ``feature network'' is used to map inputs into a latent feature space, where a GP with a ``base kernel'' acts; the resulting model is then trained in an end-to-end fashion. In this work, we introduce the ``deep ensemble kernel learning'' (DEKL) model, which is a special case of DKL. In DEKL, a linear base kernel is used, enabling exact optimization of the base kernel hyperparameters and a scalable inference method that does not require approximation by inducing points. We also represent the feature network as a concatenation of an ensemble of learner networks with a common architecture, allowing for easy model parallelism. We show that DEKL is able to approximate any kernel if the number of learners in the ensemble is arbitrarily large. Comparing the DEKL model to DKL and deep ensemble (DE) baselines on both synthetic and real-world regression tasks, we find that DEKL often outperforms both baselines in terms of predictive performance and that the DEKL learners tend to be more diverse (i.e., less correlated with one another) compared to the DE learners.
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GENERATIVE MODEL-ENHANCED HUMAN MOTION PREDICTION
ood generative human motion prediction task human motion natural heterogeneity compositionality actions robustness
The task of predicting human motion is complicated by the natural heterogeneity and compositionality of actions, necessitating robustness to distributional shifts as far as out-of-distribution (OoD). Here we formulate a new OoD benchmark based on the Human3.6M and CMU motion capture datasets, and introduce a hy- brid framework for hardening discriminative architectures to OoD failure by aug- menting them with a generative model. When applied to current state-of-the-art discriminative models, we show that the proposed approach improves OoD ro- bustness without sacrificing in-distribution performance, and can theoretically facilitate model interpretability. We suggest human motion predictors ought to be constructed with OoD challenges in mind, and provide an extensible general framework for hard- ening diverse discriminative architectures to extreme distributional shift.
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GG-GAN: A Geometric Graph Generative Adversarial Network
GAN generative adversarial network WGAN GNN graph neural network generative model graph
We study the fundamental problem of graph generation. Specifically, we treat graph generation from a geometric perspective by associating each node with a position in space and then connecting the edges based on a similarity function. We then provide new solutions to the key challenges that prevent the widespread application of this classical geometric interpretation: (1) modeling complex relations, (2) modeling isomorphic graphs consistently, and (3) fully exploiting the latent distribution.
Our main contribution is dubbed as the geometric graph (GG) generative adversarial network (GAN), which is a Wasserstein GAN that addresses the above challenges. GG-GAN is permutation equivariant and easily scales to generate graphs of tens of thousands of nodes. GG-GAN also strikes a good trade-off between novelty and modeling the distribution statistics, being competitive or surpassing the state-of-the-art methods that are either slower or that are non-equivariant, or that exploit problem-specific knowledge.
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ProteinHypothesis: A Physics-Aware Chain of Multi-Agent RAG LLM for Hypothesis Generation in Protein Science
Hypothesis Generation Multi-Agent LLM Retrieval-augmented Generation (RAG) Protein Science
Scientific hypothesis generation is fundamental to advancing molecular biology and protein science. This study presents a novel AI-driven multi-agent framework that integrates Retrieval-Augmented Generation (RAG) with structured experimental data for automated hypothesis generation and validation. The methodology employs scientific literature retrieval, structured dataset analysis, and multi-agent evaluation, ensuring that generated hypotheses are scientifically rigorous and experimentally testable. The framework consists of three key phases: (1) Hypothesis Generation, where insights from literature and structured data are synthesized using large language models; (2) Multi-Agent Evaluation through Chain of Thoughts (CoT) mechanism, where hypotheses are assessed for internal consistency, feasibility analysis, novelty assessment, scientific impact, and scalability/generalizability; and (3) Final Selection and Validation, where high-scoring hypotheses undergo refinement using protein-specialized agents and are linked to experimental validation strategies such as molecular dynamics simulations, site-directed mutagenesis, and structural characterization. Results demonstrate the system’s ability to generate novel, high-impact hypotheses in protein stability, enzyme catalysis, ligand interactions, and biomolecular interactions, with broad applications in drug discovery, synthetic biology, and protein engineering. The study highlights the potential of AI-driven hypothesis generation in accelerating scientific discovery by integrating machine learning, structured data analysis, and multi-agent validation into research workflows. Our code is available at https://github.com/adibgpt/ProteinHypothesis.
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Deep Quotient Manifold Modeling
deep generative models manifold learning
One of the difficulties in modeling real-world data is their complex multi-manifold structure due to discrete features. In this paper, we propose quotient manifold modeling (QMM), a new data-modeling scheme that considers generic manifold structure independent of discrete features, thereby deriving efficiency in modeling and allowing generalization over untrained manifolds. QMM considers a deep encoder inducing an equivalence between manifolds; but we show it is sufficient to consider it only implicitly via a bias-regularizer we derive. This makes QMM easily applicable to existing models such as GANs and VAEs, and experiments show that these models not only present superior FID scores but also make good generalizations across different datasets. In particular, we demonstrate an MNIST model that synthesizes EMNIST alphabets.
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Unmasking Transformers: A Theoretical Approach to Data Recovery via Attention Weights
Inversion Attack Data Privacy in LLMs Optimization
In the realm of deep learning, transformers have emerged as a dominant architecture, particularly in both natural language processing and computer vision tasks. However, with their widespread adoption, concerns regarding the security and privacy of the data processed by these models have arisen. In this paper, we address a pivotal question: Can the data fed into transformers be recovered using their attention weights and outputs? We introduce a theoretical framework to tackle this problem. Specifically, we present an algorithm that aims to recover the input data $X \in \mathbb{R}^{d \times n}$ from given attention weights $W = QK^\top \in \mathbb{R}^{d \times d}$ and output $B \in \mathbb{R}^{n \times n}$ by minimizing the loss function $L(X)$. This loss function captures the discrepancy between the expected output and the actual output of the transformer. Our findings have significant implications for preventing privacy leakage from attacking open-sourced model weights, suggesting potential vulnerabilities in the model's design from a security and privacy perspective - you may need only a few steps of training to force LLMs to tell their secrets.
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Understanding the role of importance weighting for deep learning
Importance Weighting Deep Learning Implicit Bias Gradient Descent Learning Theory
The recent paper by Byrd & Lipton (2019), based on empirical observations, raises a major concern on the impact of importance weighting for the over-parameterized deep learning models. They observe that as long as the model can separate the training data, the impact of importance weighting diminishes as the training proceeds. Nevertheless, there lacks a rigorous characterization of this phenomenon. In this paper, we provide formal characterizations and theoretical justifications on the role of importance weighting with respect to the implicit bias of gradient descent and margin-based learning theory. We reveal both the optimization dynamics and generalization performance under deep learning models. Our work not only explains the various novel phenomenons observed for importance weighting in deep learning, but also extends to the studies where the weights are being optimized as part of the model, which applies to a number of topics under active research.
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Tracking the progress of Language Models by extracting their underlying Knowledge Graphs
Language Models NLP Knowledge Graphs Probe tasks Word2Vec GloVe ELMo BERT RoBERTa XLNet ALBERT T5 GPT2
The state of the art of language models, previously dominated by pre-trained word embeddings, is now being pushed forward by large pre-trained contextual representations. This success has driven growing interest to understand what these models encode inside their inner workings. Despite this, understanding their semantic skills has been elusive, often leading to unsuccessful, non-conclusive, or contradictory results among different works. In this work, we define a probing classifier that we use to extract the underlying knowledge graph of nine of the currently most influential language models, including word embeddings, context encoders, and text generators. This probe is based on concept relatedness, grounded on WordNet. Our results show that this knowledge is present in all the models, but has several inaccuracies. Furthermore, we show that the different pre-training strategies and architectures lead to different model biases. We conduct a systematic evaluation to discover specific factors that explain why some concepts are challenging for the different families of models. We hope our insights will motivate the future development of models that capture concepts more precisely.
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Distribution-Based Invariant Deep Networks for Learning Meta-Features
invariant neural networks universal approximation meta-feature learning
Recent advances in deep learning from probability distributions successfully achieve classification or regression from distribution samples, thus invariant under permutation of the samples. The first contribution of the paper is to extend these neural architectures to achieve invariance under permutation of the features, too. The proposed architecture, called Dida, inherits the NN properties of universal approximation, and its robustness with respect to Lipschitz-bounded transformations of the input distribution is established. The second contribution is to empirically and comparatively demonstrate the merits of the approach on two tasks defined at the dataset level. On both tasks, Dida learns meta-features supporting the characterization of a (labelled) dataset. The first task consists of predicting whether two dataset patches are extracted from the same initial dataset. The second task consists of predicting whether the learning performance achieved by a hyper-parameter configuration under a fixed algorithm (ranging in k-NN, SVM, logistic regression and linear SGD) dominates that of another configuration, for a dataset extracted from the OpenML benchmarking suite. On both tasks, Dida outperforms the state of the art: DSS and Dataset2Vec architectures, as well as the models based on the hand-crafted meta-features of the literature.
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Understanding the Relationship between Prompts and Response Uncertainty in Large Language Models
Large language models Uncertainty Quantification Explanability Model Response Uncertainty Quantification Prompt Informativeness
Large language models (LLMs) are widely used in decision-making, but their reliability, especially in critical tasks like healthcare, is not well-established. Therefore, understanding how LLMs reason and make decisions is crucial for their safe deployment. This paper investigates how the uncertainty of responses generated by LLMs relates to the information provided in the input prompt. Leveraging the insight that LLMs learn to infer latent concepts during pretraining, we propose a prompt-response concept model that explains how LLMs generate responses and helps understand the relationship between prompts and response uncertainty. We show that the uncertainty decreases as the prompt's informativeness increases, similar to epistemic uncertainty. Our detailed experimental results on real-world datasets validate our proposed model.
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Extreme Memorization via Scale of Initialization
Scale of initialization Memorization Overfitting Generalization Generalization Measure Understanding Deep Learning
We construct an experimental setup in which changing the scale of initialization strongly impacts the implicit regularization induced by SGD, interpolating from good generalization performance to completely memorizing the training set while making little progress on the test set. Moreover, we find that the extent and manner in which generalization ability is affected depends on the activation and loss function used, with sin activation being the most extreme. In the case of the homogeneous ReLU activation, we show that this behavior can be attributed to the loss function. Our empirical investigation reveals that increasing the scale of initialization correlates with misalignment of representations and gradients across examples in the same class. This insight allows us to device an alignment measure over gradients and representations which can capture this phenomenon. We demonstrate that our alignment measure correlates with generalization of deep models trained on image classification tasks.
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Meta-Learning with Neural Tangent Kernels
meta-learning neural tangent kernel
Model Agnostic Meta-Learning (MAML) has emerged as a standard framework for meta-learning, where a meta-model is learned with the ability of fast adapting to new tasks. However, as a double-looped optimization problem, MAML needs to differentiate through the whole inner-loop optimization path for every outer-loop training step, which may lead to both computational inefficiency and sub-optimal solutions. In this paper, we generalize MAML to allow meta-learning to be defined in function spaces, and propose the first meta-learning paradigm in the Reproducing Kernel Hilbert Space (RKHS) induced by the meta-model's Neural Tangent Kernel (NTK). Within this paradigm, we introduce two meta-learning algorithms in the RKHS, which no longer need a sub-optimal iterative inner-loop adaptation as in the MAML framework. We achieve this goal by 1) replacing the adaptation with a fast-adaptive regularizer in the RKHS; and 2) solving the adaptation analytically based on the NTK theory. Extensive experimental studies demonstrate advantages of our paradigm in both efficiency and quality of solutions compared to related meta-learning algorithms. Another interesting feature of our proposed methods is that they are demonstrated to be more robust to adversarial attacks and out-of-distribution adaptation than popular baselines, as demonstrated in our experiments.
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Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filtering
Neural ODE conditional expectation irregular-observed data modelling
Combinations of neural ODEs with recurrent neural networks (RNN), like GRU-ODE-Bayes or ODE-RNN are well suited to model irregularly observed time series. While those models outperform existing discrete-time approaches, no theoretical guarantees for their predictive capabilities are available. Assuming that the irregularly-sampled time series data originates from a continuous stochastic process, the $L^2$-optimal online prediction is the conditional expectation given the currently available information. We introduce the Neural Jump ODE (NJ-ODE) that provides a data-driven approach to learn, continuously in time, the conditional expectation of a stochastic process. Our approach models the conditional expectation between two observations with a neural ODE and jumps whenever a new observation is made. We define a novel training framework, which allows us to prove theoretical guarantees for the first time. In particular, we show that the output of our model converges to the $L^2$-optimal prediction. This can be interpreted as solution to a special filtering problem. We provide experiments showing that the theoretical results also hold empirically. Moreover, we experimentally show that our model outperforms the baselines in more complex learning tasks and give comparisons on real-world datasets.
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Contextual Transformation Networks for Online Continual Learning
Continual Learning
Continual learning methods with fixed architectures rely on a single network to learn models that can perform well on all tasks.
As a result, they often only accommodate common features of those tasks but neglect each task's specific features. On the other hand, dynamic architecture methods can have a separate network for each task, but they are too expensive to train and not scalable in practice, especially in online settings.
To address this problem, we propose a novel online continual learning method named ``Contextual Transformation Networks” (CTN) to efficiently model the \emph{task-specific features} while enjoying neglectable complexity overhead compared to other fixed architecture methods.
Moreover, inspired by the Complementary Learning Systems (CLS) theory, we propose a novel dual memory design and an objective to train CTN that can address both catastrophic forgetting and knowledge transfer simultaneously.
Our extensive experiments show that CTN is competitive with a large scale dynamic architecture network and consistently outperforms other fixed architecture methods under the same standard backbone. Our implementation can be found at \url{https://github.com/phquang/Contextual-Transformation-Network}.
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Polynomial Graph Convolutional Networks
Graph Convolutional Networks Graph Neural Network Deep Learning Structured Data Machine Learning on Graphs
Graph Convolutional Neural Networks (GCNs) exploit convolution operators, based on some neighborhood aggregating scheme, to compute representations of graphs. The most common convolution operators only exploit local topological information. To consider wider topological receptive fields, the mainstream approach is to non-linearly stack multiple Graph Convolutional (GC) layers. In this way, however, interactions among GC parameters at different levels pose a bias on the flow of topological information. In this paper, we propose a different strategy, considering a single graph convolution layer that independently exploits neighbouring nodes at different topological distances, generating decoupled representations for each of them. These representations are then processed by subsequent readout layers. We implement this strategy introducing the Polynomial Graph Convolution (PGC) layer, that we prove being more expressive than the most common convolution operators and their linear stacking. Our contribution is not limited to the definition of a convolution operator with a larger receptive field, but we prove both theoretically and experimentally that the common way multiple non-linear graph convolutions are stacked limits the neural network expressiveness. Specifically, we show that a Graph Neural Network architecture with a single PGC layer achieves state of the art performance on many commonly adopted graph classification benchmarks.
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Environment Predictive Coding for Embodied Agents
Self-supervised learning visual navigation
We introduce environment predictive coding, a self-supervised approach to learn environment-level representations for embodied agents. In contrast to prior work on self-supervised learning for images, we aim to jointly encode a series of images gathered by an agent as it moves about in 3D environments. We learn these representations via a zone prediction task, where we intelligently mask out portions of an agent's trajectory and predict them from the unmasked portions, conditioned on the agent's camera poses. By learning such representations on a collection of videos, we demonstrate successful transfer to multiple downstream navigation-oriented tasks. Our experiments on the photorealistic 3D environments of Gibson and Matterport3D show that our method outperforms the state-of-the-art on challenging tasks with only a limited budget of experience.
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Geometry of Program Synthesis
Program Synthesis Singular Learning Theory Bayesian Inference MCMC
We present a new perspective on program synthesis in which programs may be identified with singularities of analytic functions. As an example, Turing machines are synthesised from input-output examples by propagating uncertainty through a smooth relaxation of a universal Turing machine. The posterior distribution over weights is approximated using Markov chain Monte Carlo and bounds on the generalisation error of these models is estimated using the real log canonical threshold, a geometric invariant from singular learning theory.
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Learning a Transferable Scheduling Policy for Various Vehicle Routing Problems based on Graph-centric Representation Learning
Vehicle Routing Problem Multiple Traveling Salesmen Problem Capacitated Vehicle Routing Problem Reinforcement Learning Graph Neural Network
Reinforcement learning has been used to learn to solve various routing problems. however, most of the algorithm is restricted to finding an optimal routing strategy for only a single vehicle. In addition, the trained policy under a specific target routing problem is not able to solve different types of routing problems with different objectives and constraints. This paper proposes an reinforcement learning approach to solve the min-max capacitated multi vehicle routing problem (mCVRP), the problem seeks to minimize the total completion time for multiple vehicles whose one-time traveling distance is constrained by their fuel levels to serve the geographically distributed customer nodes. The method represents the relationships among vehicles, customers, and fuel stations using relationship-specific graphs to consider their topological relationships and employ graph neural network (GNN) to extract the graph's embedding to be used to make a routing action. We train the proposed model using the random mCVRP instance with different numbers of vehicles, customers, and refueling stations. We then validate that the trained policy solve not only new mCVRP problems with different complexity (weak transferability but also different routing problems (CVRP, mTSP, TSP) with different objectives and constraints (storing transferability).
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Simple Augmentation Goes a Long Way: ADRL for DNN Quantization
Reinforcement Learning Quantization mixed precision augmented deep reinforcement learning DNN
Mixed precision quantization improves DNN performance by assigning different layers with different bit-width values. Searching for the optimal bit-width for each layer, however, remains a challenge. Deep Reinforcement Learning (DRL) shows some recent promise. It however suffers instability due to function approximation errors, causing large variances in the early training stages, slow convergence, and suboptimal policies in the mixed-precision quantization problem. This paper proposes augmented DRL (ADRL) as a way to alleviate these issues. This new strategy augments the neural networks in DRL with a complementary scheme to boost the performance of learning. The paper examines the effectiveness of ADRL both analytically and empirically, showing that it can produce more accurate quantized models than the state of the art DRL-based quantization while improving the learning speed by 4.5-64 times.
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From Points to Functions: Infinite-dimensional Representations in Diffusion Models
diffusion-based models representation learning score model trajectory representation attention
Diffusion-based generative models learn to iteratively transfer unstructured noise to a complex target distribution as opposed to Generative Adversarial Networks (GANs) or the decoder of Variational Autoencoders (VAEs) which produce samples from the target distribution in a single step. Thus, in diffusion models every sample is naturally connected to a random trajectory which is a solution to a learned stochastic differential equation (SDE). Generative models are only concerned with the final state of this trajectory that delivers samples from the desired distribution. \cite{abstreiter2021diffusion} showed that these stochastic trajectories can be seen as continuous filters that wash out information along the way. Consequently, there is an intermediate time step at which the preserved information is optimal for a given downstream task. In this work, we show that a combination of information content from different time steps gives a strictly better representation for the downstream task. We introduce an attention and recurrence based modules that ``learn to mix'' information content of various time-steps such that the resultant representation leads to superior performance in downstream tasks.
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Network Architecture Search for Domain Adaptation
domain adaptation feature generator network architecture search transferable representations popular networks tasks
Deep networks have been used to learn transferable representations for domain adaptation. Existing deep domain adaptation methods systematically employ popular hand-crafted networks designed specifically for image-classification tasks, leading to sub-optimal domain adaptation performance. In this paper, we present Neural Architecture Search for Domain Adaptation (NASDA), a principle framework that leverages differentiable neural architecture search to derive the optimal network architecture for domain adaptation task. NASDA is designed with two novel training strategies: neural architecture search with multi-kernel Maximum Mean Discrepancy to derive the optimal architecture, and adversarial training between a feature generator and a batch of classifiers to consolidate the feature generator. We demonstrate experimentally that NASDA leads to state-of-the-art performance on several domain adaptation benchmarks.
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Ablation Path Saliency
image classification interpretability feature attribution saliency ablation
We consider the saliency problem for black-box classification. In image classification, this means highlighting the part of the image that is most relevant for the current decision.
We cast the saliency problem as finding an optimal ablation path between two images. An ablation path consists of a sequence of ever smaller masks, joining the current image to a reference image in another decision region. The optimal path will stay as long as possible in the current decision region. This approach extends the ablation tests in [Sturmfels et al. (2020)]. The gradient of the corresponding objective function is closely related to the integrated gradient method [Sundararajan et al. (2017)]. In the saturated case (when the classifier outputs a binary value) our method would reduce to the meaningful perturbation approach [Fong & Vedaldi (2017)], since crossing the decision boundary as late as
possible would then be equivalent to finding the smallest possible mask lying on
the decision boundary.
Our interpretation provides geometric understanding of existing saliency methods, and suggests a novel approach based on ablation path optimisation.
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CTRLsum: Towards Generic Controllable Text Summarization
controllable text summarization
Current summarization systems yield generic summaries that are disconnected from users' preferences and expectations. To address this limitation, we present CTRLsum, a novel framework for controllable summarization. Our approach enables users to control multiple aspects of generated summaries by interacting with the summarization system through textual input in the form of a set of keywords or descriptive prompts. Using a single unified model, CTRLsum is able to achieve a broad scope of summary manipulation at inference time without requiring additional human annotations or pre-defining a set of control aspects during training. We quantitatively demonstrate the effectiveness of our approach on three domains of summarization datasets and five control aspects: 1) entity-centric and 2) length-controllable summarization, 3) contribution summarization on scientific papers, 4) invention purpose summarization on patent filings, and 5) question-guided summarization on news articles in a reading comprehension setting. Moreover, when used in a standard, uncontrolled summarization setting, CTRLsum achieves state-of-the-art results on the CNN/DailyMail dataset.
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Seeds, Contexts, and Tongues: Decoding the Drivers of Hallucination in Language Models
Language Models (LLMs) Hallucination Detection Semantic Entropy Natural Language Processing (NLP) Pidgin Language Cross-Lingual Analysis
This study investigates hallucinations in Large Language Models (LLMs) during free-form text generation, particularly in Nigerian and Western contexts. We study how hyperparameters, cultural background, and prompt language (particularly, Nigerian Pidgin) affect hallucination rates. Using semantic entropy as an indicator of hallucination, we examine response variability in Llama 3.1 outputs and cluster them using the entailment model microsoft/deberta-base-mnli to identify semantic similarity. We then use these clusters to calculate semantic entropy (the variation in meanings of the LLM's responses) using a variant of Shannon entropy to quantify hallucination likelihood. Our findings shed light on ways to improve LLM reliability and consistency across linguistic and cultural situations.
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CPT: Efficient Deep Neural Network Training via Cyclic Precision
Efficient training low precision training
Low-precision deep neural network (DNN) training has gained tremendous attention as reducing precision is one of the most effective knobs for boosting DNNs' training time/energy efficiency. In this paper, we attempt to explore low-precision training from a new perspective as inspired by recent findings in understanding DNN training: we conjecture that DNNs' precision might have a similar effect as the learning rate during DNN training, and advocate dynamic precision along the training trajectory for further boosting the time/energy efficiency of DNN training. Specifically, we propose Cyclic Precision Training (CPT) to cyclically vary the precision between two boundary values which can be identified using a simple precision range test within the first few training epochs. Extensive simulations and ablation studies on five datasets and eleven models demonstrate that CPT's effectiveness is consistent across various models/tasks (including classification and language modeling). Furthermore, through experiments and visualization we show that CPT helps to (1) converge to a wider minima with a lower generalization error and (2) reduce training variance which we believe opens up a new design knob for simultaneously improving the optimization and efficiency of DNN training.
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Learning and Generalization in Univariate Overparameterized Normalizing Flows
generalization unsupervised learning nfs learning supervised learning overparameterized neural networks hidden layer learn models
In supervised learning, it is known that overparameterized neural networks with one hidden layer provably and efficiently learn and generalize, when trained using Stochastic Gradient Descent (SGD). In contrast, the benefit of overparameterization in unsupervised learning is not well understood. Normalizing flows (NFs) learn to map complex real-world distributions into simple base distributions, and constitute an important class of models in unsupervised learning for sampling and density estimation. In this paper, we theoretically and empirically analyze these models when the underlying neural network is one hidden layer overparametrized network. On the one hand we provide evidence that for a class of NFs, overparametrization hurts training. On the other, we prove that another class of NFs, with similar underlying networks can efficiently learn any reasonable data distribution under minimal assumptions. We extend theoretical ideas on learning and generalization from overparameterized neural networks in supervised learning to overparameterized normalizing flows in unsupervised learning. We also provide experimental validation to support our theoretical analysis in practice.
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Prune 'n Predict: Optimizing LLM Decision-making with Conformal Prediction
Large Language Models Conformal Prediction Uncertainty Quantification Prompting MCQ Tool Learning Agentic AI Test-time Scaling
Large language models (LLMs) are empowering decision-making in several applications, including tool or API usage and answering multiple-choice questions (MCQs). However, incorrect outputs pose significant risks in high-stakes domains like healthcare and finance. To quantify LLM uncertainty and thereby mitigate these risks, recent works employ conformal prediction (CP), a model- and distribution-agnostic framework that uses LLM outputs to generate a \emph{prediction set} containing the true answer with high probability. Leveraging CP, we propose \emph{conformal revision of questions} (CROQ), which revises the question by narrowing down the available choices to those in the prediction set and asking the LLM the revised question. We expect LLMs to be more accurate on revised questions with fewer choices. Furthermore, we expect CROQ to be effective when the prediction sets from CP are small. Commonly used logit scores often lead to large sets, diminishing CROQ's effectiveness. To overcome this, we propose CP-OPT, an optimization framework to learn scores that minimize set sizes while maintaining coverage. Our extensive experiments on MMLU, ToolAlpaca, and TruthfulQA datasets with multiple LLMs show that CROQ improves accuracy over the standard inference, with more pronounced gains when paired with CP-OPT.
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Sparse matrix products for neural network compression
Compression sparsity
Over-parameterization of neural networks is a well known issue that comes along with their great performance. Among the many approaches proposed to tackle this problem, low-rank tensor decompositions are largely investigated to compress deep neural networks. Such techniques rely on a low-rank assumption of the layer weight tensors that does not always hold in practice. Following this observation, this paper studies sparsity inducing techniques to build new sparse matrix product layer for high-rate neural networks compression. Specifically, we explore recent advances in sparse optimization to replace each layer's weight matrix, either convolutional or fully connected, by a product of sparse matrices. Our experiments validate that our approach provides a better compression-accuracy trade-off than most popular low-rank-based compression techniques.
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Learning Representation in Colour Conversion
Color representation VAE Color space Unsupervised learning
Colours can be represented in an infinite set of spaces highlighting distinct features. Here, we investigated the impact of colour spaces on the encoding capacity of a visual system that is subject to information compression, specifically variational autoencoders (VAEs) where bottlenecks are imposed. To this end, we propose a novel unsupervised task: colour space conversion (ColourConvNets). We trained several instances of VAEs whose input and output are in different colour spaces, e.g. from RGB to CIE L*a*b* (in total five colour spaces were examined). This allowed us to systematically study the influence of input-output colour spaces on the encoding efficiency and learnt representation. Our evaluations demonstrate that ColourConvNets with decorrelated output colour spaces produce higher quality images, also evident in pixel-wise low-level metrics such as colour difference ($\Delta E$), peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). We also assessed the ColourConvNets' capacity to reconstruct the global content in two downstream tasks: image classification (ImageNet) and scene segmentation (COCO). Our results show a 5-10% performance boost for decorrelating ColourConvNets with respect to the baseline network (whose input and output are RGB). Furthermore, we thoroughly analysed the finite embedding space of Vector Quantised VAEs with three different methods (single feature, hue shift and linear transformation). The interpretations reached with these techniques are in agreement suggesting that (i) luminance and chromatic information are encoded in separate embedding vectors, and (ii) the structure of the network's embedding space is determined by the output colour space.
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Generalisation Guarantees For Continual Learning With Orthogonal Gradient Descent
Continual Learning Neural Tangent Kernel Optimisation
In Continual Learning settings, deep neural networks are prone to Catastrophic Forgetting. Orthogonal Gradient Descent (Farajtabar et al., 2019) was proposed to tackle the challenge. However, no theoretical guarantees have been proven yet. We present a theoretical framework to study Continual Learning algorithms in the NTK regime. This framework comprises closed form expression of the model through tasks and proxies for transfer learning, generalisation and tasks similarity. In this framework, we prove that OGD is robust to Catastrophic Forgetting then derive the first generalisation bound for SGD and OGD for Continual Learning. Finally, we study the limits of this framework in practice for OGD and highlight the importance of the NTK variation for Continual Learning.
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Feature Integration and Group Transformers for Action Proposal Generation
temporal action proposal transformer video analysis
The task of temporal action proposal generation (TAPG) aims to provide high-quality video segments, i.e., proposals that potentially contain action events. The performance of tackling the TAPG task heavily depends on two key issues, feature representation and scoring mechanism. To simultaneously take account of both aspects, we introduce an attention-based model, termed as FITS, to address the issues for retrieving high-quality proposals. We first propose a novel Feature-Integration (FI) module to seamlessly fuse two-stream features concerning their interaction to yield a robust video segment representation. We then design a group of Transformer-driven Scorers (TS) to gain the temporal contextual supports over the representations for estimating the starting or ending boundary of an action event. Unlike most previous work to estimate action boundaries without considering the long-range temporal neighborhood, the proposed action-boundary co-estimation mechanism in TS leverages the bi-directional contextual supports for such boundary estimation, which shows the advantage of removing several false-positive boundary predictions. We conduct experiments on two challenging datasets, ActivityNet-1.3 and THUMOS-14. The experimental results demonstrate that the proposed FITS model consistently outperforms state-of-the-art TAPG methods.
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Object-Centric Learning as Nested Optimization
nested optimization iterative amortized inference object-centric learning
Various iterative algorithms have shown promising results in unsupervised decomposition simple visual scenes into representations of humans could intuitively consider objects, but all with different algorithmic and implementational design choices for making them work.
In this paper, we ask what the underlying computational problem that all of these iterative approaches are solving.
We show that these approaches can all be viewed as instances of algorithms for solving a particular nested optimization problem whose inner optimization is that of maximizing the ELBO with respect to a set of independently initialized parameters for each datapoint.
We lastly discuss how our nested optimization formulation reveals connections to similar problems studied in other fields, enabling us to leverage tools developed in these other fields to improve our object-centric learning methods.
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Addressing the Topological Defects of Disentanglement
Disentanglement Equivariance Topology Representation theory Character theory
A core challenge in Machine Learning is to disentangle natural factors of variation in data (e.g. object shape vs pose). A popular approach to disentanglement consists in learning to map each of these factors to distinct subspaces of a model's latent representation. However, this approach has shown limited empirical success to date. Here, we show that this approach to disentanglement introduces topological defects (i.e. discontinuities in the encoder) for a broad family of transformations acting on images ---encompassing simple affine transformations such as rotations and translations. Moreover, motivated by classical results from group representation theory, we propose an alternative, more flexible approach to disentanglement which relies on distributed equivariant operators, potentially acting on the entire latent space. We theoretically and empirically demonstrate the effectiveness of our approach to disentangle affine transformations. Our work lays a theoretical foundation for the recent success of a new generation of models using distributed operators for disentanglement (see Discussion).
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GASP: Efficient Black-Box Generation of Adversarial Suffixes for Jailbreaking LLMs
LLM Safety Jailbreak Attacks Adversarial Vulnerability
LLMs have demonstrated remarkable capabilities but remain highly susceptible to adversarial prompts despite extensive efforts for safety alignment, raising serious security concerns for their real-world adoptions. Existing jailbreak attacks rely on manual heuristics or computationally expensive optimization techniques, both struggling with generalization and efficiency. In this paper, we introduce GASP, a novel black-box attack framework that leverages latent Bayesian optimization to generate human-readable adversarial suffixes. Unlike prior methods, GASP efficiently explores continuous embedding spaces, optimizing for strong adversarial suffixes while preserving prompt coherence. We evaluate our method across multiple LLMs, showing its ability to produce natural and effective jailbreak prompts. Compared with alternatives, GASP significantly improves attack success rates and reduces computation costs, offering a scalable approach for red-teaming LLMs.
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DIET-SNN: A Low-Latency Spiking Neural Network with Direct Input Encoding & Leakage and Threshold Optimization
Spiking neural networks threshold optimization leak optimization input encoding deep convolutional networks
Bio-inspired spiking neural networks (SNNs), operating with asynchronous binary signals (or spikes) distributed over time, can potentially lead to greater computational efficiency on event-driven hardware. The state-of-the-art SNNs suffer from high inference latency, resulting from inefficient input encoding, and sub-optimal settings of the neuron parameters (firing threshold, and membrane leak). We propose DIET-SNN, a low latency deep spiking network that is trained with gradient descent to optimize the membrane leak and the firing threshold along with other network parameters (weights). The membrane leak and threshold for each layer of the SNN are optimized with end-to-end backpropagation to achieve competitive accuracy at reduced latency. The analog pixel values of an image are directly applied to the input layer of DIET-SNN without the need to convert to spike-train. The first convolutional layer is trained to convert inputs into spikes where leaky-integrate-and-fire (LIF) neurons integrate the weighted inputs and generate an output spike when the membrane potential crosses the trained firing threshold. The trained membrane leak controls the flow of input information and attenuates irrelevant inputs to increase the activation sparsity in the convolutional and linear layers of the network. The reduced latency combined with high activation sparsity provides large improvements in computational efficiency. We evaluate DIET-SNN on image classification tasks from CIFAR and ImageNet datasets on VGG and ResNet architectures. We achieve top-1 accuracy of 69% with 5 timesteps (inference latency) on the ImageNet dataset with 12x less compute energy than an equivalent standard ANN. Additionally, DIET-SNN performs 20-500x faster inference compared to other state-of-the-art SNN models.
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Ensembles of Generative Adversarial Networks for Disconnected Data
GANs ensembles disconnected data
Most computer vision datasets are composed of disconnected sets, such as images of different objects. We prove that distributions of this type of data cannot be represented with a continuous generative network without error, independent of the learning algorithm used. Disconnected datasets can be represented in two ways: with an ensemble of networks or with a single network using a truncated latent space. We show that ensembles are more desirable than truncated distributions for several theoretical and computational reasons. We construct a regularized optimization problem that rigorously establishes the relationships between a single continuous GAN, an ensemble of GANs, conditional GANs, and Gaussian Mixture GANs. The regularization can be computed efficiently, and we show empirically that our framework has a performance sweet spot that can be found via hyperparameter tuning. The ensemble framework provides better performance than a single continuous GAN or cGAN while maintaining fewer total parameters.
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Return-Based Contrastive Representation Learning for Reinforcement Learning
reinforcement learning auxiliary task representation learning contrastive learning
Recently, various auxiliary tasks have been proposed to accelerate representation learning and improve sample efficiency in deep reinforcement learning (RL). However, existing auxiliary tasks do not take the characteristics of RL problems into consideration and are unsupervised. By leveraging returns, the most important feedback signals in RL, we propose a novel auxiliary task that forces the learnt representations to discriminate state-action pairs with different returns. Our auxiliary loss is theoretically justified to learn representations that capture the structure of a new form of state-action abstraction, under which state-action pairs with similar return distributions are aggregated together. Empirically, our algorithm outperforms strong baselines on complex tasks in Atari games and DeepMind Control suite, and achieves even better performance when combined with existing auxiliary tasks.
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Self-supervised Visual Reinforcement Learning with Object-centric Representations
self-supervision autonomous learning object-centric representations visual reinforcement learning
Autonomous agents need large repertoires of skills to act reasonably on new tasks that they have not seen before. However, acquiring these skills using only a stream of high-dimensional, unstructured, and unlabeled observations is a tricky challenge for any autonomous agent. Previous methods have used variational autoencoders to encode a scene into a low-dimensional vector that can be used as a goal for an agent to discover new skills. Nevertheless, in compositional/multi-object environments it is difficult to disentangle all the factors of variation into such a fixed-length representation of the whole scene. We propose to use object-centric representations as a modular and structured observation space, which is learned with a compositional generative world model.
We show that the structure in the representations in combination with goal-conditioned attention policies helps the autonomous agent to discover and learn useful skills. These skills can be further combined to address compositional tasks like the manipulation of several different objects.
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Investigation of Latent Time-Scales in Neural ODE Surrogate Models
Neural ordinary differential equations Reduced-order model Surrogate model Time-scales Dynamical systems
This work explores autoencoder-based neural ordinary differential equation (neural ODE) surrogate models for advection-dominated dynamical systems. Alongside predictive demonstrations, physical insight into the sources of model acceleration (i.e., how the neural ODE achieves its acceleration) is the scope of the current study. Such investigations are performed by quantifying the effect of neural ODE components on latent system time-scales using eigenvalue analysis of dynamical system Jacobians. This work uncovers the key role played by the training trajectory length on the latent system time-scales: larger trajectory lengths correlate with an increase in limiting neural ODE time-scales, and optimal neural ODEs are found to recover the largest time-scales of the full-order (ground-truth) system. Demonstration studies are performed using datasets sourced from numerical solutions of the Kuramoto-Sivashinsky equation and hydrogen-air channel detonations (compressible reacting Navier-Stokes equations).
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RBF-PINN: NON-FOURIER POSITIONAL EMBEDDING IN PHYSICS-INFORMED NEURAL NETWORKS
PHYSICS-INFORMED NEURAL NETWORKS; PINNs; Feature Mapping; Positional Embedding
While many recent Physics-Informed Neural Networks (PINNs) variants have had considerable success in solving Partial Differential Equations, the empirical benefits of feature mapping drawn from the broader Neural Representations research have been largely overlooked. We highlight the limitations of widely used Fourier-based feature mapping in certain situations and suggest the use of the conditionally positive definite Radial Basis Function. The empirical findings demonstrate the effectiveness of our approach across a variety of forward and inverse problem cases. Our method can be seamlessly integrated into coordinate-based input neural networks and contribute to the wider field of PINNs research.
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Efficient Graph Neural Architecture Search
graph neural network neural architecture search automated machine learning
Recently, graph neural networks (GNN) have been demonstrated effective in various graph-based tasks.
To obtain state-of-the-art (SOTA) data-specific GNN architectures, researchers turn to the neural architecture search (NAS) methods.
However, it remains to be a challenging problem to conduct efficient architecture search for GNN.
In this work, we present a novel framework for Efficient GrAph Neural architecture search (EGAN).
By designing a novel and expressive search space, an efficient one-shot NAS method based on stochastic relaxation and natural gradient is proposed.
Further, to enable architecture search in large graphs, a transfer learning paradigm is designed.
Extensive experiments, including node-level and graph-level tasks, are conducted. The results show that the proposed EGAN can obtain SOTA data-specific architectures, and reduce the search cost by two orders of magnitude compared to existing NAS baselines.
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Learning Reasoning Paths over Semantic Graphs for Video-grounded Dialogues
video-grounded dialogues reasoning paths semantic graphs
Compared to traditional visual question answering, video-grounded dialogues require additional reasoning over dialogue context to answer questions in a multi-turn setting. Previous approaches to video-grounded dialogues mostly use dialogue context as a simple text input without modelling the inherent information flows at the turn level. In this paper, we propose a novel framework of Reasoning Paths in Dialogue Context (PDC). PDC model discovers information flows among dialogue turns through a semantic graph constructed based on lexical components in each question and answer. PDC model then learns to predict reasoning paths over this semantic graph. Our path prediction model predicts a path from the current turn through past dialogue turns that contain additional visual cues to answer the current question. Our reasoning model sequentially processes both visual and textual information through this reasoning path and the propagated features are used to generate the answer. Our experimental results demonstrate the effectiveness of our method and provide additional insights on how models use semantic dependencies in a dialogue context to retrieve visual cues.
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Pea-KD: Parameter-efficient and accurate Knowledge Distillation
BERT Deep Learning Natural Language Processing Transformer Knowledge Distillation Parameter Sharing
How can we efficiently compress a model while maintaining its performance? Knowledge Distillation (KD) is one of the widely known methods for model compression. In essence, KD trains a smaller student model based on a larger teacher model and tries to retain the teacher model's level of performance as much as possible. However, the existing KD methods suffer from the following limitations. First, since the student model is small in absolute size, it inherently lacks model complexity. Second, the absence of an initial guide for the student model makes it difficult for the student to imitate the teacher model to its fullest. Conventional KD methods yield low performance due to these limitations.
In this paper, we propose Pea-KD (Parameter-efficient and accurate Knowledge Distillation), a novel approach to KD. Pea-KD consists of two main parts: Shuffled Parameter Sharing (SPS) and Pretraining with Teacher's Predictions (PTP). Using this combination, we are capable of alleviating the KD's limitations. SPS is a new parameter sharing method that allows greater model complexity for the student model. PTP is a KD-specialized initialization method, which can act as a good initial guide for the student. When combined, this method yields a significant increase in student model's performance. Experiments conducted on different datasets and tasks show that the proposed approach improves the student model's performance by 4.4% on average in four GLUE tasks, outperforming existing KD baselines by significant margins.
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Zero-shot Transfer Learning for Gray-box Hyper-parameter Optimization
Hyper-parameter Optimization Transfer Learning Meta-learning
Zero-shot hyper-parameter optimization refers to the process of selecting hyper- parameter configurations that are expected to perform well for a given dataset upfront, without access to any observations of the losses of the target response. Existing zero-shot approaches are posed as initialization strategies for Bayesian Optimization and they often rely on engineered meta-features to measure dataset similarity, operating under the assumption that the responses of similar datasets behaves similarly with respect to the same hyper-parameters. Solutions for zero- shot HPO are embarrassingly parallelizable and thus can reduce vastly the required wallclock time of learning a single model. We propose a very simple HPO model called Gray-box Zero(0)-Shot Initialization (GROSI) as a conditional parametric surrogate that learns a universal response model by exploiting the relationship between the hyper-parameters and the dataset meta-features directly. In contrast to existing HPO solutions, we achieve transfer of knowledge without engineered meta- features, but rather through a shared model that is trained simultaneously across all datasets. We design and optimize a novel loss function that allows us to regress from the dataset/hyper-parameter pair unto the response. Experiments on 120 datasets demonstrate the strong performance of GROSI, compared to conventional initialization strategies. We also show that by fine-tuning GROSI to the target dataset, we can outperform state-of-the-art sequential HPO algorithms.
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A Unified View on Graph Neural Networks as Graph Signal Denoising
Graph Neural Networks Graph Signal Denoising Smoothness
Graph Neural Networks (GNNs) have risen to prominence in learning representations for graph structured data. A single GNN layer typically consists of a feature transformation and a feature aggregation operation. The former normally uses feed-forward networks to transform features, while the latter aggregates the transformed features over the graph. Numerous recent works have proposed GNN models with different designs in the aggregation operation. In this work, we establish mathematically that the aggregation processes in a group of representative GNN models including GCN, GAT, PPNP, and APPNP can be regarded as (approximately) solving a graph denoising problem with a smoothness assumption. Such a unified view across GNNs not only provides a new perspective to understand a variety of aggregation operations but also enables us to develop a unified graph neural network framework UGNN. To demonstrate its promising potential, we instantiate a novel GNN model, ADA-UGNN, derived from UGNN, to handle graphs with adaptive smoothness across nodes. Comprehensive experiments show the effectiveness of ADA-UGNN.
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CNNs as Inverse Problem Solvers and Double Network Superresolution
superresolution convolutional neural network sparse representation inverse problem
In recent years Convolutional Neural Networks (CNN) have been used extensively for Superresolution (SR). In this paper, we use inverse problem and sparse representation solutions to form a mathematical basis for CNN operations. We show how a single neuron is able to provide the optimum solution for inverse problem, given a low resolution image dictionary as an operator. Introducing a new concept called Representation Dictionary Duality, we show that CNN elements (filters) are trained to be representation vectors and then, during reconstruction, used as dictionaries. In the light of theoretical work, we propose a new algorithm which uses two networks with different structures that are separately trained with low and high coherency image patches and show that it performs faster compared to the state-of-the-art algorithms while not sacrificing from performance.
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Generating unseen complex scenes: are we there yet?
generative adversarial networks conditional scene generation zero-shot generalization out of distribution
Although recent complex scene conditional generation models generate increasingly appealing scenes, it is very hard to assess which models perform better and why. This is often due to models being trained to fit different data splits, and defining their own experimental setups. In this paper, we propose a methodology to compare complex scene conditional generation models, and provide an in-depth analysis that assesses the ability of each model to (1) fit the training distribution and hence perform well on seen conditionings, (2) to generalize to unseen conditionings composed of seen object combinations, and (3) generalize to unseen conditionings composed of unseen object combinations. As a result, we observe that recent methods are able to generate recognizable scenes given seen conditionings, and exploit compositionality to generalize to unseen conditionings with seen object combinations. However, all methods suffer from noticeable image quality degradation when asked to generate images from conditionings composed of unseen object combinations. Moreover, through our analysis, we identify the advantages of different pipeline components, and find that (1) encouraging compositionality through instance-wise spatial conditioning normalizations increases robustness to both types of unseen conditionings, (2) using semantically aware losses such as the scene-graph perceptual similarity helps improve some dimensions of the generation process, and (3) enhancing the quality of generated masks and the quality of the individual objects are crucial steps to improve robustness to both types of unseen conditionings.
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Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration
exploration; autonomous goal setting; diversity; unsupervised learning; deep neural network
Intrinsically motivated goal exploration algorithms enable machines to discover repertoires of policies that produce a diversity of effects in complex environments. These exploration algorithms have been shown to allow real world robots to acquire skills such as tool use in high-dimensional continuous state and action spaces. However, they have so far assumed that self-generated goals are sampled in a specifically engineered feature space, limiting their autonomy. In this work, we propose an approach using deep representation learning algorithms to learn an adequate goal space. This is a developmental 2-stage approach: first, in a perceptual learning stage, deep learning algorithms use passive raw sensor observations of world changes to learn a corresponding latent space; then goal exploration happens in a second stage by sampling goals in this latent space. We present experiments with a simulated robot arm interacting with an object, and we show that exploration algorithms using such learned representations can closely match, and even sometimes improve, the performance obtained using engineered representations.
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IsarStep: a Benchmark for High-level Mathematical Reasoning
mathematical reasoning dataset benchmark reasoning transformer
A well-defined benchmark is essential for measuring and accelerating research progress of machine learning models. In this paper, we present a benchmark for high-level mathematical reasoning and study the reasoning capabilities of neural sequence-to-sequence models. We build a non-synthetic dataset from the largest repository of proofs written by human experts in a theorem prover. The dataset has a broad coverage of undergraduate and research-level mathematical and computer science theorems. In our defined task, a model is required to fill in a missing intermediate proposition given surrounding proofs. This task provides a starting point for the long-term goal of having machines generate human-readable proofs automatically. Our experiments and analysis reveal that while the task is challenging, neural models can capture non-trivial mathematical reasoning. We further design a hierarchical transformer that outperforms the transformer baseline.
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The role of Disentanglement in Generalisation
disentanglement compositionality compositional generalization generalisation generative models variational autoencoders
Combinatorial generalisation — the ability to understand and produce novel combinations of familiar elements — is a core capacity of human intelligence that current AI systems struggle with. Recently, it has been suggested that learning disentangled representations may help address this problem. It is claimed that such representations should be able to capture the compositional structure of the world which can then be combined to support combinatorial generalisation. In this study, we systematically tested how the degree of disentanglement affects various forms of generalisation, including two forms of combinatorial generalisation that varied in difficulty. We trained three classes of variational autoencoders (VAEs) on two datasets on an unsupervised task by excluding combinations of generative factors during training. At test time we ask the models to reconstruct the missing combinations in order to measure generalisation performance. Irrespective of the degree of disentanglement, we found that the models supported only weak combinatorial generalisation. We obtained the same outcome when we directly input perfectly disentangled representations as the latents, and when we tested a model on a more complex task that explicitly required independent generative factors to be controlled. While learning disentangled representations does improve interpretability and sample efficiency in some downstream tasks, our results suggest that they are not sufficient for supporting more difficult forms of generalisation.
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Bias Correction of Learned Generative Models via Likelihood-free Importance Weighting
importance bias bias correction learned generative models samples model likelihood ratio learned generative model biased statistics data distribution
A learned generative model often gives biased statistics relative to the underlying data distribution. A standard technique to correct this bias is by importance weighting samples from the model by the likelihood ratio under the model and true distributions. When the likelihood ratio is unknown, it can be estimated by training a probabilistic classifier to distinguish samples from the two distributions. In this paper, we employ this likelihood-free importance weighting framework to correct for the bias in using state-of-the-art deep generative models.We find that this technique consistently improves standard goodness-of-fit metrics for evaluating the sample quality of state-of-the-art generative models, suggesting reduced bias. Finally, we demonstrate its utility on representative applications in a) data augmentation for classification using generative adversarial networks, and b) model-based policy evaluation using off-policy data.
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Response Modeling of Hyper-Parameters for Deep Convolutional Neural Networks
Hyper-Parameter Optimization Response Surface Modeling Convolution Neural Network Low-Rank Factorization
Hyper-parameter optimization (HPO) is critical in training high performing Deep Neural Networks (DNN). Current methodologies fail to define an analytical response surface and remain a training bottleneck due to their use of additional internal hyper-parameters and lengthy evaluation cycles. We demonstrate that the low-rank factorization of the convolution weights of intermediate layers of a CNN can define an analytical response surface. We quantify how this surface acts as an auxiliary to optimizing training metrics. We introduce a dynamic tracking algorithm -- autoHyper -- that performs HPO on the order of hours for various datasets including ImageNet and requires no manual tuning. Our method -- using a single RTX2080Ti -- is able to select a learning rate within 59 hours for AdaM on ResNet34 applied to ImageNet and improves in testing accuracy by 4.93% over the default learning rate. In contrast to previous methods, we empirically prove that our algorithm and response surface generalize well across model, optimizer, and dataset selection removing the need for extensive domain knowledge to achieve high levels of performance.
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Graph2Seq: Scalable Learning Dynamics for Graphs
scalable dynamics graphs graphs neural networks general purpose algorithms graph data
Neural networks are increasingly used as a general purpose approach to learning algorithms over graph structured data. However, techniques for representing graphs as real-valued vectors are still in their infancy. Recent works have proposed several approaches (e.g., graph convolutional networks), but as we show in this paper, these methods have difficulty generalizing to large graphs. In this paper we propose Graph2Seq, an embedding framework that represents graphs as an infinite time-series. By not limiting the representation to a fixed dimension, Graph2Seq naturally scales to graphs of arbitrary size. Moreover, through analysis of a formal computational model we show that an unbounded sequence is necessary for scalability. Graph2Seq is also reversible, allowing full recovery of the graph structure from the sequence. Experimental evaluations of Graph2Seq on a variety of combinatorial optimization problems show strong generalization and strict improvement over state of the art.
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Multi-agent Policy Optimization with Approximatively Synchronous Advantage Estimation
multi-agent reinforcement learning policy optimization advantage estimation credit assignment
Cooperative multi-agent tasks require agents to deduce their own contributions with shared global rewards, known as the challenge of credit assignment. General methods for policy based multi-agent reinforcement learning to solve the challenge introduce differentiate value functions or advantage functions for individual agents. In multi-agent system, polices of different agents need to be evaluated jointly. In order to update polices synchronously, such value functions or advantage functions also need synchronous evaluation. However, in current methods, value functions or advantage functions use counter-factual joint actions which are evaluated asynchronously, thus suffer from natural estimation bias. In this work, we propose the approximatively synchronous advantage estimation. We first derive the marginal advantage function, an expansion from single-agent advantage function to multi-agent system. Further more, we introduce a policy approximation for synchronous advantage estimation, and break down the multi-agent policy optimization problem into multiple sub-problems of single-agent policy optimization. Our method is compared with baseline algorithms on StarCraft multi-agent challenges, and shows the best performance on most of the tasks.
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CITRIS: Causal Identifiability from Temporal Intervened Sequences
Causal Representation Learning Causal Identifiability Generalization
We propose CITRIS, a variational framework that learns causal representations from temporal sequences of images with interventions. In contrast to the recent literature, CITRIS exploits temporality and the observation of intervention targets to identify scalar and multidimensional causal factors. Furthermore, by introducing a normalizing flow, we extend CITRIS to leverage and disentangle representations obtained by already pretrained autoencoders. Extending previous results on scalar causal factors, we prove identifiability in a more general setting, in which only some components of a causal factor are affected by interventions. In experiments on 3D rendered image sequences, CITRIS outperforms previous methods on recovering the underlying causal variables, and can even generalize to unseen instantiations of causal factors, opening future research areas in sim-to-real generalization.
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Linear Last-iterate Convergence in Constrained Saddle-point Optimization
Saddle-point Optimization Optimistic Mirror Decent Optimistic Gradient Descent Ascent Optimistic Multiplicative Weights Update Last-iterate Convergence Game Theory
Optimistic Gradient Descent Ascent (OGDA) and Optimistic Multiplicative Weights Update (OMWU) for saddle-point optimization have received growing attention due to their favorable last-iterate convergence. However, their behaviors for simple bilinear games over the probability simplex are still not fully understood --- previous analysis lacks explicit convergence rates, only applies to an exponentially small learning rate, or requires additional assumptions such as the uniqueness of the optimal solution.
In this work, we significantly expand the understanding of last-iterate convergence for OGDA and OMWU in the constrained setting. Specifically, for OMWU in bilinear games over the simplex, we show that when the equilibrium is unique, linear last-iterate convergence is achievable with a constant learning rate, which improves the result of (Daskalakis & Panageas, 2019) under the same assumption. We then significantly extend the results to more general objectives and feasible sets for the projected OGDA algorithm, by introducing a sufficient condition under which OGDA exhibits concrete last-iterate convergence rates with a constant learning rate. We show that bilinear games over any polytope satisfy this condition and OGDA converges exponentially fast even without the unique equilibrium assumption. Our condition also holds for strongly-convex-strongly-concave functions, recovering the result of (Hsieh et al., 2019). Finally, we provide experimental results to further support our theory.
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VideoFlow: A Framework for Building Visual Analysis Pipelines
Computation graph Resource Computer vision Deep learning Framework Software
The past years have witnessed an explosion of deep learning frameworks like PyTorch and TensorFlow since the success of deep neural networks. These frameworks have significantly facilitated algorithm development in multimedia research and production. However, how to easily and efficiently build an end-to-end visual analysis pipeline with these algorithms is still an open issue. In most cases, developers have to spend a huge amount of time tackling data input and output, optimizing computation efficiency, or even debugging exhausting memory leaks together with algorithm development. VideoFlow aims to overcome these challenges by providing a flexible, efficient, extensible, and secure visual analysis framework for both the academia and industry. With VideoFlow, developers can focus on the improvement of algorithms themselves, as well as the construction of a complete visual analysis workflow. VideoFlow has been incubated in the practices of smart city innovation for more than three years. It has been widely used in tens of intelligent visual analysis systems. VideoFlow will be open-sourced at \url{https://github.com/xxx/videoflow}.
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Consistency Matters: Neural ODE Parameters are Dependent on the Training Numerical Method
Differential Equations; Numerical Methods; Neural ODEs; Optimization
Neural Ordinary Differential Equations (Neural ODEs) are continuous-depth models that use an ordinary differential equation (ODE) to capture the dynamics of data. Due to their modelling capabilities several works on applications and novel architectures using Neural ODEs can be found in the literature.
In this work, we call for the attention to the need of using the same numerical method for both training and making predictions with Neural ODEs since the numerical method employed influences the prediction process, thereby impacting the loss function and introducing variance into parameter optimisation.
We provide theoretical insights into how numerical methods of varying orders or with different step sizes influence the loss function of the network. To validate our theoretical analysis, we conduct a series of simple preliminary numerical experiments employing a regression task, demonstrating how the training numerical method influences model performance for testing. Our findings underscore the need for consistency in numerical methods for training and prediction, a consideration not previously emphasised or documented in the literature.
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Counterfactual Thinking for Long-tailed Information Extraction
Information Extraction Natural Language Processing Long-tailed Classification Causal Inference
Information Extraction (IE) aims to extract structured information from unstructured texts. However, in practice, the long-tailed and imbalanced data may lead to severe bias issues for deep learning models, due to very few training instances available for the tail classes. Existing works are mainly from computer vision society, leveraging re-balancing, decoupling, transfer learning and causal inference to address this problem on image classification and scene graph generation. However, these approaches may not achieve good performance on textual data, which involves complex language structures that have been proven crucial for the IE tasks. To this end, we propose a novel framework (named CFIE) based on language structure and causal reasoning with three key ingredients. First, by fusing the syntax information to various structured causal models for mainstream IE tasks including relation extraction (RE), named entity recognition (NER), and event detection (ED), our approach is able to learn the direct effect for classification from an imbalanced dataset. Second, counterfactuals are generated based on an explicit language structure to better calculate the direct effect during the inference stage. Third, we propose a flexible debiasing approach for more robust prediction during the inference stage. Experimental results on three IE tasks across five public datasets show that our model significantly outperforms the state-of-the-art models by a large margin in terms of Mean Recall and Macro F1, achieving a relative 30% improvement in Mean Recall for 7 tail classes on the ACE2005 dataset. We also discuss some interesting findings based on our observations.
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Distributional Reinforcement Learning for Risk-Sensitive Policies
policies policy algorithm distributional reinforcement policies distributional reinforcement problem cvar risk measure distributional reinforcement learning particular
We address the problem of learning a risk-sensitive policy based on the CVaR risk measure using distributional reinforcement learning. In particular, we show that applying the distributional Bellman optimality operator with respect to a risk-based action-selection strategy overestimates the dynamic, Markovian CVaR. The resulting policies can however still be overly conservative and one often prefers to learn an optimal policy based on the static, non-Markovian CVaR. To this end, we propose a modification to the existing algorithm and show that it can indeed learn a proper CVaR-optimized policy. Our proposed approach is a simple extension of standard distributional RL algorithms and can therefore take advantage of many of the recent advances in deep RL. On both synthetic and real data, we empirically show that our proposed algorithm is able to produce a family of risk-averse policies that achieves a better tradeoff between risk and the expected return.
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Addressing Some Limitations of Transformers with Feedback Memory
Feedback Memory Transformers
Transformers have been successfully applied to sequential tasks despite being feedforward networks. Unlike recurrent neural networks, Transformers use attention to capture temporal relations while processing input tokens in parallel. While this parallelization makes them computationally efficient, it restricts the model from fully exploiting the sequential nature of the input. The representation at a given layer can only access representations from lower layers, rather than the higher level representations already available. In this work, we propose the Feedback Transformer architecture that exposes all previous representations to all future representations, meaning the lowest representation of the current timestep is formed from the highest-level abstract representation of the past. We demonstrate on a variety of benchmarks in language modeling, machine translation, and reinforcement learning that the increased representation capacity can create small, shallow models with much stronger performance than comparable Transformers.
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Object-centric Generative Models for Spatial Scene Understanding
generative models spatial scene agent able scene constituent objects raw sensory data core ability robots environment
Representing a scene and its constituent objects from raw sensory data is a core ability for enabling robots to interact with their environment. In this paper, we propose a novel system for scene understanding, leveraging object-centric generative models. We demonstrate an agent that is able to learn and reason about 3D objects in an unsupervised fashion and is able to infer object category and pose in an allocentric reference frame. Our agent can infer actions to reach a given, object-relative target viewpoint in simulation, outperforming a supervised baseline trained on the same object set.
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Learning Aggregation Functions
Deep learning Neural networks Relational and structured data Aggregation functions
Learning on sets is increasingly gaining attention in the machine learning community, due to its widespread applicability. Typically, representations over sets are computed by using fixed aggregation functions such as sum or maximum. However, recent results showed that universal function representation by sum- (or max-) decomposition requires either highly discontinuous (and thus poorly learnable) mappings, or a latent dimension equal to the maximum number of elements in the set. To mitigate this problem, we introduce LAF (Learning Aggregation Functions), a learnable aggregator for sets of arbitrary cardinality. LAF can approximate several extensively used aggregators (such as average, sum, maximum) as well as more complex functions (e.g. variance and skewness). We report experiments on semi-synthetic and real data showing that LAF outperforms state-of-the-art sum- (max-) decomposition architectures such as DeepSets and library-based architectures like Principal Neighborhood Aggregation.
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Lipschitz-Bounded Equilibrium Networks
Adversarial Robustness Equilibrium Networks Neural ODE
This paper introduces new parameterizations of equilibrium neural networks, i.e.
networks defined by implicit equations. This model class includes standard multilayer
and residual networks as special cases. The new parameterization admits
a Lipschitz bound during training via unconstrained optimization, i.e. no projections
or barrier functions are required. Lipschitz bounds are a common proxy for
robustness and appear in many generalization bounds. Furthermore, compared to
previous works we show well-posedness (existence of solutions) under less restrictive
conditions on the network weights and more natural assumptions on the
activation functions: that they are monotone and slope restricted. These results
are proved by establishing novel connections with convex optimization, operator
splitting on non-Euclidean spaces, and contracting neural ODEs. In image classification
experiments we show that the Lipschitz bounds are very accurate and
improve robustness to adversarial attacks.
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Uncertainty Quantification for Fourier Neural Operators
Partial Differential Equations Fourier Neural Operator Uncertainty Quantification Ensemble Predictions Bayesian Deep Learning Laplace Approximation
In medium-term weather forecasting, deep learning techniques have emerged as a strong alternative to classical numerical solvers for partial differential equations that describe the underlying physical system. While well-established deep learning models such as Fourier Neural Operators are effective at predicting future states of the system, extending these methods to provide ensemble predictions still poses a challenge. However, it is known that ensemble predictions are crucial in real-world applications such as weather, where local dynamics are not necessarily accounted for due to the coarse data resolution. In this paper, we explore different methods for generating ensemble predictions with Fourier Neural Operators trained on a simple one-dimensional PDE dataset: input perturbations and training for multiple outputs via a statistical loss function. Moreover, we formulate a new Laplace approximation for Fourier layers and show that it exhibits better uncertainty quantification for short training runs.
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Deep Partition Aggregation: Provable Defenses against General Poisoning Attacks
bagging ensemble robustness certificate poisoning smoothing
Adversarial poisoning attacks distort training data in order to corrupt the test-time behavior of a classifier. A provable defense provides a certificate for each test sample, which is a lower bound on the magnitude of any adversarial distortion of the training set that can corrupt the test sample's classification.
We propose two novel provable defenses against poisoning attacks: (i) Deep Partition Aggregation (DPA), a certified defense against a general poisoning threat model, defined as the insertion or deletion of a bounded number of samples to the training set --- by implication, this threat model also includes arbitrary distortions to a bounded number of images and/or labels; and (ii) Semi-Supervised DPA (SS-DPA), a certified defense against label-flipping poisoning attacks. DPA is an ensemble method where base models are trained on partitions of the training set determined by a hash function. DPA is related to both subset aggregation, a well-studied ensemble method in classical machine learning, as well as to randomized smoothing, a popular provable defense against evasion (inference) attacks. Our defense against label-flipping poison attacks, SS-DPA, uses a semi-supervised learning algorithm as its base classifier model: each base classifier is trained using the entire unlabeled training set in addition to the labels for a partition. SS-DPA significantly outperforms the existing certified defense for label-flipping attacks (Rosenfeld et al., 2020) on both MNIST and CIFAR-10: provably tolerating, for at least half of test images, over 600 label flips (vs. < 200 label flips) on MNIST and over 300 label flips (vs. 175 label flips) on CIFAR-10. Against general poisoning attacks where no prior certified defenses exists, DPA can certify $\geq$ 50% of test images against over 500 poison image insertions on MNIST, and nine insertions on CIFAR-10. These results establish new state-of-the-art provable defenses against general and label-flipping poison attacks. Code is available at https://github.com/alevine0/DPA
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Contrastive Learning of Medical Visual Representations from Paired Images and Text
visual representation learning contrastive learning medical image understanding natural language processing
Learning visual representations of medical images is core to medical image understanding but its progress has been held back by the small size of hand-labeled datasets. Existing work commonly relies on transferring weights from ImageNet pretraining, which is suboptimal due to drastically different image characteristics, or rule-based label extraction from the textual report data paired with medical images, which is inaccurate and hard to generalize. We propose an alternative unsupervised strategy to learn medical visual representations directly from the naturally occurring pairing of images and textual data. Our method of pretraining medical image encoders with the paired text data via a bidirectional contrastive objective between the two modalities is domain-agnostic, and requires no additional expert input. We test our method by transferring our pretrained weights to 4 medical image classification tasks and 2 zero-shot retrieval tasks, and show that our method leads to image representations that considerably outperform strong baselines in most settings. Notably, in all 4 classification tasks, our method requires only 10% as much labeled training data as an ImageNet initialized counterpart to achieve better or comparable performance, demonstrating superior data efficiency.
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Box-To-Box Transformation for Modeling Joint Hierarchies
Box embeddings Representation Learning Joint Hierarchy transitive relations knowledge graph embedding relational learning.
Learning representations of entities and relations in knowledge graphs is an active area of research, with much emphasis placed on choosing the appropriate geometry to capture tree-like structures. Box embeddings (Vilnis et al., 2018; Li et al., 2019; Dasgupta et al., 2020), which represent concepts as n-dimensional hyperrectangles, are capable of embedding trees by training on a subset of the transitive closure. In Patel et al. (2020), the authors demonstrate that only the transitive reduction is required, and further extend box embeddings to capture joint hierarchies by augmenting the graph with new nodes. While it is possible to represent joint hierarchies with this method, the parameters for each hierarchy are decoupled, making generalization between hierarchies infeasible. In this work, we introduce a learned box-to-box transformation which respects the geometric structure of the box embeddings. We demonstrate that this not only improves the capability of modeling cross-hierarchy compositional edges but is also capable of generalizing from a subset of the transitive reduction.
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Reset-Free Lifelong Learning with Skill-Space Planning
reset-free lifelong reinforcement learning
The objective of \textit{lifelong} reinforcement learning (RL) is to optimize agents which can continuously adapt and interact in changing environments. However, current RL approaches fail drastically when environments are non-stationary and interactions are non-episodic. We propose \textit{Lifelong Skill Planning} (LiSP), an algorithmic framework for lifelong RL based on planning in an abstract space of higher-order skills. We learn the skills in an unsupervised manner using intrinsic rewards and plan over the learned skills using a learned dynamics model. Moreover, our framework permits skill discovery even from offline data, thereby reducing the need for excessive real-world interactions. We demonstrate empirically that LiSP successfully enables long-horizon planning and learns agents that can avoid catastrophic failures even in challenging non-stationary and non-episodic environments derived from gridworld and MuJoCo benchmarks.
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Play to Grade: Grading Interactive Coding Games as Classifying Markov Decision Process
Deep Reinforcement Learning Education Automated Grading Program Testing
Contemporary coding education often present students with the task of developing programs that have user interaction and complex dynamic systems, such as mouse based games. While pedagogically compelling, grading such student programs requires dynamic user inputs, therefore they are difficult to grade by unit tests. In this paper we formalize the challenge of grading interactive programs as a task of classifying Markov Decision Processes (MDPs). Each student's program fully specifies an MDP where the agent needs to operate and decide, under reasonable generalization, if the dynamics and reward model of the input MDP conforms to a set of latent MDPs. We demonstrate that by experiencing a handful of latent MDPs millions of times, we can use the agent to sample trajectories from the input MDP and use a classifier to determine membership. Our method drastically reduces the amount of data needed to train an automatic grading system for interactive code assignments and present a challenge to state-of-the-art reinforcement learning generalization methods. Together with Code.org, we curated a dataset of 700k student submissions, one of the largest dataset of anonymized student submissions to a single assignment. This Code.org assignment had no previous solution for automatically providing correctness feedback to students and as such this contribution could lead to meaningful improvement in educational experience.
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Emergent Communication Fine-tuning (EC-FT) for Pretrained Language Models
emergent communication fine-tuning unsupervised machine translation multingual language models mBART multimodal NLP multimodal pretraining machine translation
It has recently been argued that the currently dominant paradigm in NLP of pretraining on text-only corpora will not yield robust natural language understanding systems. One strain of this argumentation highlights the need for grounded, goal-oriented, and interactive language learning. In this position paper, we articulate how Emergent Communication (EC) can be used in conjunction with large pretrained language models as a `Fine-Tuning' (FT) step (hence, EC-FT) in order to provide them with supervision from such learning scenarios. We discuss methodological issues and difficulties with making this work, and then illustrate the overall idea with a case study in unsupervised machine translation, before concluding with a discussion on the relation to multimodal pretraining.
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A new framework for tensor PCA based on trace invariants
Tensor Principal Component Analysis Tensor decomposition trace invariant
We consider the Principal Component Analysis (PCA) problem for tensors $T \in (\mathbb{R}^n)^{\otimes k}$ of large dimension $n$ and of arbitrary order $k\geq 3$. It consists in recovering a spike $v_0^{\otimes k}$ (related to a signal vector $v_0 \in \mathbb{R}^n$) corrupted by a Gaussian noise tensor $Z \in (\mathbb{R}^n)^{\otimes k}$ such that $T=\beta v_0^{\otimes k} + Z$ where $\beta$ is the signal-to-noise ratio. In this paper, we propose a new framework based on tools developed by the theoretical physics community to address this important problem. They consist in trace invariants of tensors built by judicious contractions (extension of matrix product) of the indices of the tensor $T$. Inspired by these tools, we introduce a new process that builds for each invariant a matrix whose top eigenvector is correlated to the signal for $\beta$ sufficiently large. Then, we give examples of classes of invariants for which we demonstrate that this correlation happens above the best algorithmic threshold ($\beta\geq n^{k/4}$) known so far. This method has many algorithmic advantages: (i) it provides a detection algorithm linear in time and that has only $O(1)$ memory requirements (ii) the algorithms are very suitable for parallel architectures and have a lot of potential of optimization given the simplicity of the mathematical tools involved (iii) experimental results show an improvement of the state of the art for the symmetric tensor PCA. Furthermore, this framework allows more general applications by being able to theoretically study the recovery of a spike in the form of $v_1 \otimes \dots \otimes v_k$ with different dimensions ($T \in \mathbb{R}^{n_1\times n_2\times \dots \times n_k}$ with $n_1,\dots, n_k \in \mathbb{N}$) as well as the recovery of a sum of different orthogonal spikes. We provide experimental results to these different cases that match well with our theoretical findings.
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Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning
Reinforcement Learning Memory Mapping Device Placement Evolutionary Algorithms
For deep neural network accelerators, memory movement is both energetically expensive and can bound computation. Therefore, optimal mapping of tensors to memory hierarchies is critical to performance. The growing complexity of neural networks calls for automated memory mapping instead of manual heuristic approaches; yet the search space of neural network computational graphs have previously been prohibitively large. We introduce Evolutionary Graph Reinforcement Learning (EGRL), a method designed for large search spaces, that combines graph neural networks, reinforcement learning, and evolutionary search. A set of fast, stateless policies guide the evolutionary search to improve its sample-efficiency. We train and validate our approach directly on the Intel NNP-I chip for inference. EGRL outperforms policy-gradient, evolutionary search and dynamic programming baselines on BERT, ResNet-101 and ResNet-50. We additionally achieve 28-78% speed-up compared to the native NNP-I compiler on all three workloads.
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MultiSTOP: Solving Functional Equations with Reinforcement Learning
Reinforcement Learning Functional Equation Physics Machine Learning
We develop MultiSTOP, a Reinforcement Learning framework for solving functional equations in physics. This new methodology is also able to find actual numerical solutions instead of bounds. We extend the original BootSTOP algorithm by adding multiple constraints derived from domain-specific knowledge, even in integral form, to improve the accuracy of the solution. We investigate a particular equation in a one-dimensional Conformal Field Theory.
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Large Language Models Are Innate Crystal Structure Generators
Crystal Structure Generation Large Language Models Evolutionary Search
Crystal structure generation is fundamental to materials discovery, enabling the prediction of novel materials with desired properties. While existing approaches leverage Large Language Models (LLMs) through extensive fine-tuning on materials databases, we show that pre-trained LLMs can inherently generate stable crystal structures without additional training. Our novel framework MatLLMSearch integrates pre-trained LLMs with evolutionary search algorithms, achieving a 78.38% metastable rate validated by machine learning interatomic potentials and 31.7% DFT-verified stability via quantum mechanical calculations, outperforming specialized models such as CrystalTextLLM. Beyond crystal structure generation, we further demonstrate that our framework can be readily adapted to diverse materials design tasks, including crystal structure prediction and multi-objective optimization of properties such as deformation energy and bulk modulus, all without fine-tuning. These results establish pre-trained LLMs as versatile and effective tools for materials discovery, opening up new venues for crystal structure generation with reduced computational overhead and broader accessibility.
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Why Are Kronecker Products So Effective?
kronecker quaternion parameter efficient tensor decomposition SVD
In this blog post we will review the layer scheme proposed by the authors of the paper "Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with $$1/n$$ Parameters", which links quaternion-based Neural Networks with the Kronecker Product, and later explain how the Kronecker Product provides a connection between the paper and a parallel line of research into parameter efficient Neural Networks for Computer Vision based on Singular Value Decomposition.
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On the Identifiability of Nonlinear ICA with Unconditional Priors
nonlinear ica identifiability auxiliary variables unconditional priors independent latent sources assumptions independent component analysis ica observable nonlinear mixtures major unsolved problem
Nonlinear independent component analysis (ICA) aims to recover the underlying marginally independent latent sources from their observable nonlinear mixtures. The identifiability of nonlinear ICA is a major unsolved problem in unsupervised learning. Recent breakthroughs reformulate the standard marginal independence assumption of sources as conditional independence given some auxiliary variables (e.g., class labels) as weak supervision or inductive bias. However, the modified setting might not be applicable in many scenarios that do not have auxiliary variables. We explore an alternative path and consider only assumptions on the mixing process, such as independent influences. We show under these assumptions that the marginally independent latent sources can be identified from the nonlinear mixtures up to a component-wise (linear) transformation and a permutation, thus providing an identifiability result of nonlinear ICA without auxiliary variables. We provide an estimation method and validate the theoretical results experimentally.
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simple_rl: Reproducible Reinforcement Learning in Python
reinforcement learning python experiments new library open source
Conducting reinforcement-learning experiments can be a complex and timely process. A full experimental pipeline will typically consist of a simulation of an environment, an implementation of one or many learning algorithms, a variety of additional components designed to facilitate the agent-environment interplay, and any requisite analysis, plotting, and logging thereof. In light of this complexity, this paper introduces simple rl, a new open source library for carrying out reinforcement learning experiments in Python 2 and 3 with a focus on simplicity. The goal of simple_rl is to support seamless, reproducible methods for running reinforcement learning experiments. This paper gives an overview of the core design philosophy of the package, how it differs from existing libraries, and showcases its central features.
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Pretrain-to-Finetune Adversarial Training via Sample-wise Randomized Smoothing
Adversarial Robustness Provable Adversarial Defense Sample-wise Randomized Smoothing.
Developing certified models that can provably defense adversarial perturbations is important in machine learning security. Recently, randomized smoothing, combined with other techniques (Cohen et al., 2019; Salman et al., 2019), has been shown to be an effective method to certify models under $l_2$ perturbations. Existing work for certifying $l_2$ perturbations added the same level of Gaussian noise to each sample. The noise level determines the trade-off between the test accuracy and the average certified robust radius. We propose to further improve the defense via sample-wise randomized smoothing, which assigns different noise levels to different samples. Specifically, we propose a pretrain-to-finetune framework that first pretrains a model and then adjusts the noise levels for higher performance based on the model’s outputs. For certification, we carefully allocate specific robust regions for each test sample. We perform extensive experiments on CIFAR-10 and MNIST datasets and the experimental results demonstrate that our method can achieve better accuracy-robustness trade-off in the transductive setting.
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Provably robust classification of adversarial examples with detection
Adversarial robustness robust deep learning
Adversarial attacks against deep networks can be defended against either by building robust classifiers or, by creating classifiers that can \emph{detect} the presence of adversarial perturbations. Although it may intuitively seem easier to simply detect attacks rather than build a robust classifier, this has not bourne out in practice even empirically, as most detection methods have subsequently been broken by adaptive attacks, thus necessitating \emph{verifiable} performance for detection mechanisms. In this paper, we propose a new method for jointly training a provably robust classifier and detector. Specifically, we show that by introducing an additional "abstain/detection" into a classifier, we can modify existing certified defense mechanisms to allow the classifier to either robustly classify \emph{or} detect adversarial attacks. We extend the common interval bound propagation (IBP) method for certified robustness under $\ell_\infty$ perturbations to account for our new robust objective, and show that the method outperforms traditional IBP used in isolation, especially for large perturbation sizes. Specifically, tests on MNIST and CIFAR-10 datasets exhibit promising results, for example with provable robust error less than $63.63\%$ and $67.92\%$, for $55.6\%$ and $66.37\%$ natural error, for $\epsilon=8/255$ and $16/255$ on the CIFAR-10 dataset, respectively.
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"Auction Learning as a Two Player Game": GANs (?) for Mechanism Design
mechanism design deep learning auctions game theory
Designing strategyproof, revenue-maximizing auctions is an important task, but it is surprisingly difficult -- even in some seemingly trivial cases, nothing is known about the optimal auction design. Motivated by this lack of progress, a number of recent works have proposed the use of deep neural networks as function approximators for learning strategyproof mechanisms. One of these works is "Auction Learning as a Two-Player Game", which appeared at ICLR 2021. We discuss this work, situate it in the broader context of deep learning for auctions, explain how it improves over prior techniques, and discuss the future outlook for interactions between modern deep learning and mechanism design.
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