_id stringlengths 4 10 | text stringlengths 0 18.4k | title stringlengths 0 8.56k |
|---|---|---|
d7060599 | It has long been recognized that the invariance and equivariance properties of a representation are critically important for success in many vision tasks. In this paper we present Steerable Convolutional Neural Networks, an efficient and flexible class of equivariant convolutional networks. We show that steerable CNNs ... | STEERABLE CNNS |
d209140749 | Understanding the representational power of Deep Neural Networks (DNNs) and how their structural properties (e.g., depth, width, type of activation unit) affect the functions they can compute, has been an important yet challenging question in deep learning and approximation theory. In a seminal paper, Telgarsky highlig... | Depth-Width Trade-offs for ReLU Networks via Sharkovsky's Theorem |
d245837195 | Transformers have been successful in many vision tasks, thanks to their capability of capturing long-range dependency. However, their quadratic computational complexity poses a major obstacle for applying them to vision tasks requiring dense predictions, such as object detection, feature matching, stereo, etc. We intro... | QUADTREE ATTENTION FOR VISION TRANSFORMERS |
d222310792 | Deploying Reinforcement Learning (RL) agents to solve real-world applications often requires satisfying complex system constraints. Often the constraint thresholds are incorrectly set due to the complex nature of a system or the inability to verify the thresholds offline (e.g, no simulator or reasonable offline evaluat... | BALANCING CONSTRAINTS AND REWARDS WITH META-GRADIENT D4PG |
d263131626 | Contrastive Language-Image Pre-training (CLIP) is an approach that has advanced research and applications in computer vision, fueling modern recognition systems and generative models. We believe that the main ingredient to the success of CLIP is its data and not the model architecture or pre-training objective. However... | DEMYSTIFYING CLIP DATA |
d262013288 | Self-supervised pre-training of language models usually consists in predicting probability distributions over extensive token vocabularies.In this study, we propose an innovative method that shifts away from probability prediction and instead focuses on reconstructing input embeddings in a contrastive fashion via Const... | Headless Language Models: Learning without Predicting with Contrastive Weight Tying |
d3833554 | We propose the Wasserstein Auto-Encoder (WAE)-a new algorithm for building a generative model of the data distribution. WAE minimizes a penalized form of the Wasserstein distance between the model distribution and the target distribution, which leads to a different regularizer than the one used by the Variational Auto-... | Wasserstein Auto-Encoders |
d257365819 | Binary Neural Networks (BNNs) are compact and efficient by using binary weights instead of real-valued weights. Current BNNs use latent real-valued weights during training, where hyper-parameters are inherited from real-valued networks. The interpretation of several of these hyperparameters is based on the magnitude of... | UNDERSTANDING WEIGHT-MAGNITUDE HYPERPARAMETERS IN TRAINING BINARY NETWORKS |
d232417873 | We study the problem of dynamic visual reasoning on raw videos. This is a challenging problem; currently, state-of-the-art models often require dense supervision on physical object properties and events from simulation, which are impractical to obtain in real life. In this paper, we present the Dynamic Concept Learner ... | GROUNDING PHYSICAL CONCEPTS OF OBJECTS AND EVENTS THROUGH DYNAMIC VISUAL REASONING |
d264405698 | Human feedback is commonly utilized to finetune AI assistants.But human feedback may also encourage model responses that match user beliefs over truthful ones, a behaviour known as sycophancy.We investigate the prevalence of sycophancy in models whose finetuning procedure made use of human feedback, and the potential r... | TOWARDS UNDERSTANDING SYCOPHANCY IN LANGUAGE MODELS |
d258947441 | Distillation-aware Neural Architecture Search (DaNAS) aims to search for an optimal student architecture that obtains the best performance and/or efficiency when distilling the knowledge from a given teacher model. Previous DaNAS methods have mostly tackled the search for the neural architecture for fixed datasets and ... | META-PREDICTION MODEL FOR DISTILLATION-AWARE NAS ON UNSEEN DATASETS |
d239768818 | Model efficiency is a critical aspect of developing and deploying machine learning models. Inference time and latency directly affect the user experience, and some applications have hard requirements. In addition to inference costs, model training also have direct financial and environmental impacts. Although there are... | THE EFFICIENCY MISNOMER |
d235422063 | This paper studies zero-shot domain adaptation where each domain is indexed on a multidimensional array, and we only have data from a small subset of domains. Our goal is to produce predictors that perform well on unseen domains. We propose a model which consists of a domain-invariant latent representation layer and a ... | Provable Adaptation across Multiway Domains via Representation Learning |
d247025626 | Quantifying the data uncertainty in learning tasks is often done by learning a prediction interval or prediction set of the label given the input. Two commonly desired properties for learned prediction sets are valid coverage and good efficiency (such as low length or low cardinality). Conformal prediction is a powerfu... | Efficient and Differentiable Conformal Prediction with General Function Classes |
d208857409 | We propose to reinterpret a standard discriminative classifier of p(y|x) as an energy based model for the joint distribution p(x, y). In this setting, the standard class probabilities can be easily computed as well as unnormalized values of p(x) and p(x|y). Within this framework, standard discriminative architectures m... | YOUR CLASSIFIER IS SECRETLY AN ENERGY BASED MODEL AND YOU SHOULD TREAT IT LIKE ONE |
d247693714 | To avoid collapse in self-supervised learning (SSL), a contrastive loss is widely used but often requires a large number of negative samples. Without negative samples yet achieving competitive performance, a recent work (Chen & He, 2021) has attracted significant attention for providing a minimalist simple Siamese (Sim... | HOW DOES SIMSIAM AVOID COLLAPSE WITHOUT NEGATIVE SAMPLES? A UNIFIED UNDERSTANDING WITH SELF-SUPERVISED CONTRASTIVE LEARNING |
d238419536 | Multitask learning is being increasingly adopted in applications domains like computer vision and reinforcement learning. However, optimally exploiting its advantages remains a major challenge due to the effect of negative transfer. Previous works have tracked down this issue to the disparities in gradient magnitudes a... | ROTOGRAD: GRADIENT HOMOGENIZATION IN MULTITASK LEARNING |
d208076137 | To improve real-world applications of machine learning, experienced modelers develop intuition about their datasets, their models, and how the two interact. Manual inspection of raw data-of representative samples, of outliers, of misclassifications-is an essential tool in a) identifying and fixing problems in the data,... | GENERATIVE MODELS FOR EFFECTIVE ML ON PRIVATE, DECENTRALIZED DATASETS |
d53729114 | Behavioral skills or policies for autonomous agents are conventionally learned from reward functions, via reinforcement learning, or from demonstrations, via imitation learning. However, both modes of task specification have their disadvantages: reward functions require manual engineering, while demonstrations require ... | GUIDING POLICIES WITH LANGUAGE VIA META-LEARNING |
d3297437 | Deep neural networks have enabled progress in a wide variety of applications. Growing the size of the neural network typically results in improved accuracy. As model sizes grow, the memory and compute requirements for training these models also increases. We introduce a technique to train deep neural networks using hal... | MIXED PRECISION TRAINING |
d247475977 | Vision transformers (ViTs) have gained popularity recently. Even without customized image operators such as convolutions, ViTs can yield competitive performance when properly trained on massive data. However, the computational overhead of ViTs remains prohibitive, due to stacking multi-head self-attention modules and e... | UNIFIED VISUAL TRANSFORMER COMPRESSION |
d253761014 | Existing Graph Neural Networks (GNNs) compute the message exchange between nodes by either aggregating uniformly (convolving) the features of all the neighboring nodes, or by applying a non-uniform score (attending) to the features. Recent works have shown the strengths and weaknesses of the resulting GNN architectures... | Learnable Graph Convolutional Attention Networks |
d5144625 | Neural networks that compute over graph structures are a natural fit for problems in a variety of domains, including natural language (parse trees) and cheminformatics (molecular graphs). However, since the computation graph has a different shape and size for every input, such networks do not directly support batched t... | DEEP LEARNING WITH DYNAMIC COMPUTATION GRAPHS |
d249209614 | Large-scale pretrained transformers have created milestones in text (GPT-3) and text-to-image (DALL-E and CogView) generation.Its application to video generation is still facing many challenges: The potential huge computation cost makes the training from scratch unaffordable; The scarcity and weak relevance of text-vid... | CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers |
d60441381 | Reinforcement learning (RL) agents optimize only the features specified in a reward function and are indifferent to anything left out inadvertently. This means that we must not only specify what to do, but also the much larger space of what not to do. It is easy to forget these preferences, since these preferences are ... | PREFERENCES IMPLICIT IN THE STATE OF THE WORLD |
d252873449 | We consider a hybrid reinforcement learning setting (Hybrid RL), in which an agent has access to an offline dataset and the ability to collect experience via real-world online interaction. The framework mitigates the challenges that arise in both pure offline and online RL settings, allowing for the design of simple an... | Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient |
d253018647 | In deep metric learning, the Triplet Loss has emerged as a popular method to learn many computer vision and natural language processing tasks such as facial recognition, object detection, and visual-semantic embeddings. One issue that plagues the Triplet Loss is network collapse, an undesirable phenomenon where the net... | Mathematical Justification of Hard Negative Mining via Isometric Approximation Theorem |
d246285344 | Some neurons in deep networks specialize in recognizing highly specific perceptual, structural, or semantic features of inputs. In computer vision, techniques exist for identifying neurons that respond to individual concept categories like colors, textures, and object classes. But these techniques are limited in scope,... | NATURAL LANGUAGE DESCRIPTIONS OF DEEP VISUAL FEATURES |
d260438589 | We present the All-Seeing (AS) 1 project: a large-scale data and model for recognizing and understanding everything in the open world. Using a scalable data engine that incorporates human feedback and efficient models in the loop, we create a new dataset (AS-1B) with over 1 billion regions annotated with semantic tags,... | The All-Seeing Project: Towards Panoptic Visual Recognition and Understanding of the Open World |
d53047182 | Nonlinearity is crucial to the performance of a deep (neural) network (DN). To date there has been little progress understanding the menagerie of available nonlinearities, but recently progress has been made on understanding the rôle played by piecewise affine and convex nonlinearities like the ReLU and absolute value ... | FROM HARD TO SOFT: UNDERSTANDING DEEP NETWORK NONLINEARITIES VIA VECTOR QUANTIZATION AND STATISTICAL INFERENCE |
d252872997 | Vision-language models (VLMs) such as CLIP have shown promising performance on a variety of recognition tasks using the standard zero-shot classification procedure -computing similarity between the query image and the embedded words for each category. By only using the category name, they neglect to make use of the ric... | VISUAL CLASSIFICATION VIA DESCRIPTION FROM LARGE LANGUAGE MODELS |
d264306115 | Models trained on different datasets can be merged by a weighted-averaging of their parameters, but why does it work and when can it fail?Here, we connect the inaccuracy of weighted-averaging to mismatches in the gradients and propose a new uncertainty-based scheme to improve the performance by reducing the mismatch.Th... | MODEL MERGING BY UNCERTAINTY-BASED GRADIENT MATCHING |
d14064410 | Humans learn a predictive model of the world and use this model to reason about future events and the consequences of actions. In contrast to most machine predictors, we exhibit an impressive ability to generalize to unseen scenarios and reason intelligently in these settings. One important aspect of this ability is ph... | GENERALIZABLE FEATURES FROM UNSUPERVISED LEARNING |
d222134085 | Relational regularized autoencoder (RAE) is a framework to learn the distribution of data by minimizing a reconstruction loss together with a relational regularization on the latent space. A recent attempt to reduce the inner discrepancy between the prior and aggregated posterior distributions is to incorporate sliced ... | Improving Relational Regularized Autoencoders with Spherical Sliced Fused Gromov Wasserstein |
d248506064 | Discrete variational auto-encoders (VAEs) are able to represent semantic latent spaces in generative learning. In many real-life settings, the discrete latent space consists of high-dimensional structures, and propagating gradients through the relevant structures often requires enumerating over an exponentially large l... | LEARNING DISCRETE STRUCTURED VARIATIONAL AUTO-ENCODER USING NATURAL EVOLUTION STRATEGIES |
d235899304 | Although Shapley values are theoretically appealing for explaining black-box models, they are costly to calculate and thus impractical in settings that involve large, high-dimensional models. To remedy this issue, we introduce FastSHAP, a new method for estimating Shapley values in a single forward pass using a learned... | FASTSHAP: REAL-TIME SHAPLEY VALUE ESTIMATION |
d247411075 | Fréchet Inception Distance (FID) is the primary metric for ranking models in datadriven generative modeling. While remarkably successful, the metric is known to sometimes disagree with human judgement. We investigate a root cause of these discrepancies, and visualize what FID "looks at" in generated images. We show tha... | THE ROLE OF IMAGENET CLASSES IN FRÉCHET INCEPTION DISTANCE |
d73664641 | Measuring divergence between two distributions is essential in machine learning and statistics and has various applications including binary classification, change point detection, and two-sample test. Furthermore, in the era of big data, designing divergence measure that is interpretable and can handle high-dimensiona... | Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator |
d264306305 | We introduce COSMOS, a framework for object-centric world modeling that is designed for compositional generalization (CG), i.e., high performance on unseen input scenes obtained through the composition of known visual "atoms."The central insight behind COSMOS is the use of a novel form of neurosymbolic grounding.Specif... | Neurosymbolic Grounding for Compositional World Models |
d251647158 | We prove that finding all globally optimal two-layer ReLU neural networks can be performed by solving a convex optimization program with cone constraints. Our analysis is novel, characterizes all optimal solutions, and does not leverage duality-based analysis which was recently used to lift neural network training into... | THE HIDDEN CONVEX OPTIMIZATION LANDSCAPE OF REGULARIZED TWO-LAYER RELU NETWORKS: AN EXACT CHARACTERIZATION OF OPTIMAL SOLUTIONS |
d245117817 | Sparse Blind Source Separation (BSS) has become a well established tool for a wide range of applications -for instance, in astrophysics and remote sensing. Classical sparse BSS methods, such as the Proximal Alternating Linearized Minimization (PALM) algorithm, nevertheless often suffer from a difficult hyperparameter c... | UNROLLING PALM FOR SPARSE SEMI-BLIND SOURCE SEPARATION |
d258999317 | In this work, we propose a Multi-Window Masked Autoencoder (MW-MAE) fitted with a novel Multi-Window Multi-Head Attention (MW-MHA) module that facilitates the modelling of local-global interactions in every decoder transformer block through attention heads of several distinct local and global windows.Empirical results ... | Masked Autoencoders with Multi-Window Local-Global Attention Are Better Audio Learners |
d264172506 | Guidance in conditional diffusion generation is of great importance for sample quality and controllability. However, existing guidance schemes are to be desired. On one hand, mainstream methods such as classifier guidance and classifier-free guidance both require extra training with labeled data, which is time-consumin... | ELUCIDATING THE DESIGN SPACE OF CLASSIFIER-GUIDED DIFFUSION GENERATION |
d263620126 | Multi-task learning (MTL) aims to empower a model to tackle multiple tasks simultaneously.A recent development known as task arithmetic has revealed that several models, each fine-tuned for distinct tasks, can be directly merged into a single model to execute MTL without necessitating a retraining process using the ini... | ADAMERGING: ADAPTIVE MODEL MERGING FOR MULTI-TASK LEARNING |
d244117259 | To understand how deep learning works, it is crucial to understand the training dynamics of neural networks. Several interesting hypotheses about these dynamics have been made based on empirically observed phenomena, but there exists a limited theoretical understanding of when and why such phenomena occur.In this paper... | The Three Stages of Learning Dynamics in High-dimensional Kernel Methods |
d848112 | We address the issue of limit cycling behavior in training Generative Adversarial Networks and propose the use of Optimistic Mirror Decent (OMD) for training Wasserstein GANs. Recent theoretical results have shown that optimistic mirror decent (OMD) can enjoy faster regret rates in the context of zero-sum games. WGANs ... | TRAINING GANS WITH OPTIMISM |
d252735034 | Despite the impressive progress of self-supervised learning (SSL), its applicability to low-compute networks has received limited attention.Reported performance has trailed behind standard supervised pre-training by a large margin, barring selfsupervised learning from making an impact on models that are deployed on dev... | EFFECTIVE SELF-SUPERVISED PRE-TRAINING ON LOW-COMPUTE NETWORKS WITHOUT DISTILLATION |
d208526957 | We present an image segmentation method that iteratively evolves a polygon. At each iteration, the vertices of the polygon are displaced based on the local value of a 2D shift map that is inferred from the input image via an encoder-decoder architecture. The main training loss that is used is the difference between the... | END TO END TRAINABLE ACTIVE CONTOURS VIA DIFFERENTIABLE RENDERING |
d251041128 | Predictive coding (PC) is an influential theory in computational neuroscience, which argues that the cortex forms unsupervised world models by implementing a hierarchical process of prediction error minimization. PC networks (PCNs) are trained in two phases. First, neural activities are updated to optimize the network'... | A THEORETICAL FRAMEWORK FOR INFERENCE AND LEARNING IN PREDICTIVE CODING NETWORKS |
d232290456 | This paper considers the problem of spatiotemporal object-centric reasoning in videos. Central to our approach is the notion of object permanence, i.e., the ability to reason about the location of objects as they move through the video while being occluded, contained or carried by other objects. Existing deep learning ... | HOPPER: MULTI-HOP TRANSFORMER FOR SPATIOTEMPORAL REASONING |
d235377347 | Metric learning involves learning a discriminative representation such that embeddings of similar classes are encouraged to be close, while embeddings of dissimilar classes are pushed far apart. State-of-the-art methods focus mostly on sophisticated loss functions or mining strategies. On the one hand, metric learning ... | IT TAKES TWO TO TANGO: MIXUP FOR DEEP METRIC LEARNING |
d257557818 | Time series modeling is a well-established problem, which often requires that methods (1) expressively represent complicated dependencies, (2) forecast long horizons, and (3) efficiently train over long sequences. State-space models (SSMs) are classical models for time series, and prior works combine SSMs with deep lea... | Effectively Modeling Time Series with Simple Discrete State Spaces |
d252780742 | While adversarial training is generally used as a defense mechanism, recent works show that it can also act as a regularizer. By co-training a neural network on clean and adversarial inputs, it is possible to improve classification accuracy on the clean, non-adversarial inputs. We demonstrate that, contrary to previous... | REVISITING ADAPTERS WITH ADVERSARIAL TRAINING |
d199552081 | Recently, the pre-trained language model, BERT [1], has attracted a lot of attention in natural language understanding (NLU), and achieved state-of-the-art accuracy in various NLU tasks, such as sentiment classification, natural language inference, semantic textual similarity and question answering. Inspired by the lin... | STRUCTBERT: INCORPORATING LANGUAGE STRUCTURES INTO PRE-TRAINING FOR DEEP LANGUAGE UNDERSTANDING |
d238583214 | Antibodies are versatile proteins that bind to pathogens like viruses and stimulate the adaptive immune system. The specificity of antibody binding is determined by complementarity-determining regions (CDRs) at the tips of these Y-shaped proteins. In this paper, we propose a generative model to automatically design the... | ITERATIVE REFINEMENT GRAPH NEURAL NETWORK FOR ANTIBODY SEQUENCE-STRUCTURE CO-DESIGN |
d209315801 | We propose Stochastic Weight Averaging in Parallel (SWAP), an algorithm to accelerate DNN training. Our algorithm uses large mini-batches to compute an approximate solution quickly and then refines it by averaging the weights of multiple models computed independently and in parallel. The resulting models generalize equ... | STOCHASTIC WEIGHT AVERAGING IN PARALLEL: LARGE-BATCH TRAINING THAT GENERALIZES WELL |
d258686174 | The Information Bottleneck (IB) principle offers an information-theoretic framework for analyzing the training process of deep neural networks (DNNs). Its essence lies in tracking the dynamics of two mutual information (MI) values: one between the hidden layer and the class label, and the other between the hidden layer... | Information Bottleneck Analysis of Deep Neural Networks via Lossy Compression |
d247594155 | In this paper, we study the problem of principal component analysis with generative modeling assumptions, adopting a general model for the observed matrix that encompasses notable special cases, including spiked matrix recovery and phase retrieval. The key assumption is that the underlying signal lies near the range of... | Generative Principal Component Analysis |
d256808264 | Forward-only" algorithms, which train neural networks while avoiding a backward pass, have recently gained attention as a way of solving the biologically unrealistic aspects of backpropagation. Here, we first discuss the similarities between two "forward-only" algorithms, the Forward-Forward and PEPITA frameworks, and ... | Forward Learning with Top-Down Feedback: Empirical and Analytical Characterization |
d259261998 | We present a neural network architecture designed to naturally learn a positional embedding and overcome the spectral bias towards lower frequencies faced by conventional implicit neural representation networks. Our proposed architecture, SPDER, is a simple MLP that uses an activation function composed of a sinusoidal ... | SPDER: Semiperiodic Damping-Enabled Object Representation |
d53466896 | Training activation quantized neural networks involves minimizing a piecewise constant function whose gradient vanishes almost everywhere, which is undesirable for the standard back-propagation or chain rule. An empirical way around this issue is to use a straight-through estimator (STE) (Bengio et al., 2013) in the b... | UNDERSTANDING STRAIGHT-THROUGH ESTIMATOR IN TRAINING ACTIVATION QUANTIZED NEURAL NETS |
d152282636 | In most practical settings and theoretical analysis, one assumes that a model can be trained until convergence. However, the growing complexity of machine learning datasets and models may violate such assumptions. Moreover, current approaches for hyper-parameter tuning and neural architecture search tend to be limited ... | Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints |
d246430598 | Even though fine-grained pruning techniques achieve a high compression ratio, conventional sparsity representations (such as CSR) associated with irregular sparsity degrade parallelism significantly. Practical pruning methods, thus, usually lower pruning rates (by structured pruning) to improve parallelism. In this pap... | ENCODING WEIGHTS OF IRREGULAR SPARSITY FOR FIXED-TO-FIXED MODEL COMPRESSION |
d211259030 | Recent studies have shown that many important aspects of neural network learning take place within the very earliest iterations or epochs of training. For example, sparse, trainable sub-networks emerge , gradient descent moves into a small subspace (Gur-Ari et al., 2018), and the network undergoes a critical period (Ac... | THE EARLY PHASE OF NEURAL NETWORK TRAINING |
d207930593 | We present the Compressive Transformer, an attentive sequence model which compresses past memories for long-range sequence learning. We find the Compressive Transformer obtains state-of-the-art language modelling results in the WikiText-103 and Enwik8 benchmarks, achieving 17.1 ppl and 0.97 bpc respectively. We also fi... | COMPRESSIVE TRANSFORMERS FOR LONG-RANGE SEQUENCE MODELLING |
d258714555 | While backpropagation (BP) is the mainstream approach for gradient computation in neural network training, its heavy reliance on the chain rule of differentiation constrains the designing flexibility of network architecture and training pipelines.We avoid the recursive computation in BP and develop a unified likelihood... | ONE FORWARD IS ENOUGH FOR NEURAL NETWORK TRAINING VIA LIKELIHOOD RATIO METHOD |
d252907833 | Influence function, a method from robust statistics, measures the changes of model parameters or some functions about model parameters concerning the removal or modification of training instances. It is an efficient and useful post-hoc method for studying the interpretability of machine learning models without the need... | CHARACTERIZING THE INFLUENCE OF GRAPH ELEMENTS |
d247158476 | Common wisdom in the graph neural network (GNN) community dictates that anisotropic models-in which messages sent between nodes are a function of both the source and target node-are required to achieve state-of-the-art performance. Benchmarks to date have demonstrated that these models perform better than comparable is... | DO WE NEED ANISOTROPIC GRAPH NEURAL NETWORKS? |
d258823066 | Latent variable models have become instrumental in computational neurosciencefor reasoning about neural computation. This has fostered the development of powerful offline algorithms for extracting latent neural trajectories from neural recordings. However, despite the potential of real time alternatives to give immedia... | REAL-TIME VARIATIONAL METHOD FOR LEARNING NEURAL TRAJECTORY AND ITS DYNAMICS |
d219792787 | The study of deep neural networks (DNNs) in the infinite-width limit, via the so-called neural tangent kernel (NTK) approach, has provided new insights into the dynamics of learning, generalization, and the impact of initialization. One key DNN architecture remains to be kernelized, namely, the recurrent neural network... | The Recurrent Neural Tangent Kernel |
d252907753 | A grand goal in deep learning research is to learn representations capable of generalizing across distribution shifts. Disentanglement is one promising direction aimed at aligning a model's representation with the underlying factors generating the data (e.g. color or background). Existing disentanglement methods, howev... | DISENTANGLEMENT OF CORRELATED FACTORS VIA HAUSDORFF FACTORIZED SUPPORT |
d233209878 | This paper theoretically investigates the following empirical phenomenon: given a highcomplexity network with poor generalization bounds, one can distill it into a network with nearly identical predictions but low complexity and vastly smaller generalization bounds. The main contribution is an analysis showing that the... | Generalization bounds via distillation |
d3289985 | Progress in deep learning is slowed by the days or weeks it takes to train large models. The natural solution of using more hardware is limited by diminishing returns, and leads to inefficient use of additional resources. In this paper, we present a large batch, stochastic optimization algorithm that is both faster tha... | Neumann Optimizer: A Practical Optimization Algorithm for Deep Neural Networks |
d222208723 | Subspace clustering is an unsupervised clustering technique designed to cluster data that is supported on a union of linear subspaces, with each subspace defining a cluster with dimension lower than the ambient space. Many existing formulations for this problem are based on exploiting the self-expressive property of li... | A CRITIQUE OF SELF-EXPRESSIVE DEEP SUBSPACE CLUSTERING |
d238583028 | Molecular chirality, a form of stereochemistry most often describing relative spatial arrangements of bonded neighbors around tetrahedral carbon centers, influences the set of 3D conformers accessible to the molecule without changing its 2D graph connectivity. Chirality can strongly alter (bio)chemical interactions, pa... | LEARNING 3D REPRESENTATIONS OF MOLECULAR CHIRALITY WITH INVARIANCE TO BOND ROTATIONS |
d260440513 | Conversational machine comprehension requires a deep understanding of the conversation history. To enable traditional, single-turn models to encode the history comprehensively, we introduce FLOW, a mechanism that can incorporate intermediate representations generated during the process of answering previous questions, ... | FLOWQA: GRASPING FLOW IN HISTORY FOR CONVERSATIONAL MACHINE COMPREHENSION |
d257038392 | We study whether automatically-induced prompts that effectively extract information from a language model can also be used, out-of-the-box, to probe other language models for the same information. After confirming that discrete prompts induced with the AutoPrompt algorithm outperform manual and semi-manual prompts on t... | CAN DISCRETE INFORMATION EXTRACTION PROMPTS GENERALIZE ACROSS LANGUAGE MODELS? |
d3162051 | Large deep neural networks are powerful, but exhibit undesirable behaviors such as memorization and sensitivity to adversarial examples. In this work, we propose mixup, a simple learning principle to alleviate these issues. In essence, mixup trains a neural network on convex combinations of pairs of examples and their ... | mixup: BEYOND EMPIRICAL RISK MINIMIZATION |
d210919941 | Variance reduction methods such as SVRG(Johnson & Zhang, 2013)and Spi-derBoost (Wang et al., 2018) use a mixture of large and small batch gradients to reduce the variance of stochastic gradients. Compared to SGD(Robbins & Monro, 1951), these methods require at least double the number of operations per update to model ... | VARIANCE REDUCTION WITH SPARSE GRADIENTS |
d67855688 | Prior work on neural network verification has focused on specifications that are linear functions of the output of the network, e.g., invariance of the classifier output under adversarial perturbations of the input. In this paper, we extend verification algorithms to be able to certify richer properties of neural netwo... | VERIFICATION OF NON-LINEAR SPECIFICATIONS FOR NEURAL NETWORKS |
d235313510 | Deep learning models have a propensity for fitting the entire training set even with random labels, which requires memorization of every training sample. In this paper, we explore the memorization effect in adversarial training (AT) for promoting a deeper understanding of model capacity, convergence, generalization, an... | EXPLORING MEMORIZATION IN ADVERSARIAL TRAINING |
d235359074 | Vision transformer (ViT) has recently shown its strong capability in achieving comparable results to convolutional neural networks (CNNs) on image classification. However, vanilla ViT simply inherits the same architecture from the natural language processing directly, which is often not optimized for vision application... | REGIONVIT: REGIONAL-TO-LOCAL ATTENTION FOR VISION TRANSFORMERS |
d224803680 | Ensemble methods which average over multiple neural network predictions are a simple approach to improve a model's calibration and robustness. Similarly, data augmentation techniques, which encode prior information in the form of invariant feature transformations, are effective for improving calibration and robustness.... | COMBINING ENSEMBLES AND DATA AUGMENTATION CAN HARM YOUR CALIBRATION |
d252683413 | Multi-Agent Reinforcement Learning (MARL)-where multiple agents learn to interact in a shared dynamic environment-permeates across a wide range of critical applications. While there has been substantial progress on understanding the global convergence of policy optimization methods in singleagent RL, designing and anal... | Faster Last-iterate Convergence of Policy Optimization in Zero-Sum Markov Games |
d8693672 | We propose an extension to neural network language models to adapt their prediction to the recent history. Our model is a simplified version of memory augmented networks, which stores past hidden activations as memory and accesses them through a dot product with the current hidden activation. This mechanism is very eff... | IMPROVING NEURAL LANGUAGE MODELS WITH A CONTINUOUS CACHE |
d3470398 | State-of-the-art methods for learning cross-lingual word embeddings have relied on bilingual dictionaries or parallel corpora. Recent works showed that the need for parallel data supervision can be alleviated with character-level information. While these methods showed encouraging results, they are not on par with thei... | WORD TRANSLATION WITHOUT PARALLEL DATA |
d211532691 | Exploration in sparse reward environments remains one of the key challenges of model-free reinforcement learning. Instead of solely relying on extrinsic rewards provided by the environment, many state-of-the-art methods use intrinsic rewards to encourage exploration. However, we show that existing methods fall short in... | RIDE: REWARDING IMPACT-DRIVEN EXPLORATION FOR PROCEDURALLY-GENERATED ENVIRONMENTS |
d235352583 | The recently discovered Neural Collapse (NC) phenomenon occurs pervasively in today's deep net training paradigm of driving cross-entropy (CE) loss towards zero. During NC, last-layer features collapse to their class-means, both classifiers and class-means collapse to the same Simplex Equiangular Tight Frame, and class... | NEURAL COLLAPSE UNDER MSE LOSS: PROXIMITY TO AND DYNAMICS ON THE CENTRAL PATH |
d258212627 | We present a Lipschitz continuous Transformer, called LipsFormer, to pursue training stability both theoretically and empirically for Transformer-based models. In contrast to previous practical tricks that address training instability by learning rate warmup, layer normalization, attention formulation, and weight initi... | LIPSFORMER: INTRODUCING LIPSCHITZ CONTINUITY TO VISION TRANSFORMERS |
d202540355 | We introduce the 2-simplicial Transformer, an extension of the Transformer which includes a form of higher-dimensional attention generalising the dot-product attention, and uses this attention to update entity representations with tensor products of value vectors. We show that this architecture is a useful inductive bi... | Logic and the 2-Simplicial Transformer |
d211126477 | We propose a reinforcement learning agent to solve hard exploration games by learning a range of directed exploratory policies. We construct an episodic memorybased intrinsic reward using k-nearest neighbors over the agent's recent experience to train the directed exploratory policies, thereby encouraging the agent to ... | NEVER GIVE UP: LEARNING DIRECTED EXPLORATION STRATEGIES |
d256105801 | Continuous-time (CT) modeling has proven to provide improved sample efficiency and interpretability in learning the dynamical behavior of physical systems compared to discrete-time (DT) models. However, even with numerous recent developments, the CT nonlinear state-space (NL-SS) model identification problem remains to ... | CONTINUOUS-TIME IDENTIFICATION OF DYNAMIC STATE-SPACE MODELS BY DEEP SUBSPACE ENCODING |
d252284009 | We devise deep nearest centroids (DNC), a conceptually elegant yet surprisingly effective network for large-scale visual recognition, by revisiting Nearest Centroids, one of the most classic and simple classifiers. Current deep models learn the classifier in a fully parametric manner, ignoring the latent data structure... | VISUAL RECOGNITION WITH DEEP NEAREST CENTROIDS |
d257038005 | Learning from human preferences is important for language models to match human needs and to align with human and social values.Prior works have achieved remarkable successes by learning from human feedback to understand and follow instructions.Nonetheless, these methods are either founded on hand-picked model generati... | Chain of Hindsight aligns Language Models with Feedback |
d240070335 | The aim in imitation learning is to learn effective policies by utilizing near-optimal expert demonstrations. However, high-quality demonstrations from human experts can be expensive to obtain in large number. On the other hand, it is often much easier to obtain large quantities of suboptimal or task-agnostic trajector... | TRAIL: NEAR-OPTIMAL IMITATION LEARNING WITH SUBOPTIMAL DATA |
d3626819 | We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectiona... | Deep contextualized word representations |
d201107103 | The use of deep pre-trained transformers has led to remarkable progress in a number of applications . For tasks that make pairwise comparisons between sequences, matching a given input with a corresponding label, two approaches are common: Cross-encoders performing full self-attention over the pair and Bi-encoders enco... | Poly-encoders: architectures and pre-training strategies for fast and accurate multi-sentence scoring |
d239009538 | Despite progress across a broad range of applications, Transformers have limited success in systematic generalization. The situation is especially frustrating in the case of algorithmic tasks, where they often fail to find intuitive solutions that route relevant information to the right node/operation at the right time... | THE NEURAL DATA ROUTER: ADAPTIVE CONTROL FLOW IN TRANSFORMERS IMPROVES SYSTEMATIC GENERALIZATION |
d226254532 | We present a two-stage framework for deep one-class classification. We first learn self-supervised representations from one-class data, and then build one-class classifiers on learned representations. The framework not only allows to learn better representations, but also permits building one-class classifiers that are... | LEARNING AND EVALUATING REPRESENTATIONS FOR DEEP ONE-CLASS CLASSIFICATION |
d208981547 | This work explores hypernetworks: an approach of using a one network, also known as a hypernetwork, to generate the weights for another network. Hypernetworks provide an abstraction that is similar to what is found in nature: the relationship between a genotype -the hypernetwork -and a phenotype -the main network. Thou... | HYPERNETWORKS |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.