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d244714571 | Offline policy learning (OPL) leverages existing data collected a priori for policy optimization without any active exploration. Despite the prevalence and recent interest in this problem, its theoretical and algorithmic foundations in function approximation settings remain under-developed. In this paper, we consider t... | OFFLINE NEURAL CONTEXTUAL BANDITS: PESSIMISM, OPTIMIZATION AND GENERALIZATION |
d244909194 | Efficient exploration is important for reinforcement learners to achieve high rewards. In multi-agent systems, coordinated exploration and behaviour is critical for agents to jointly achieve optimal outcomes. In this paper, we introduce a new general framework for improving coordination and performance of multi-agent r... | LIGS: LEARNABLE INTRINSIC-REWARD GENERATION SELECTION FOR MULTI-AGENT LEARNING |
d221041408 | How to make unsupervised language pre-training more efficient and less resourceintensive is an important research direction in NLP. In this paper, we focus on improving the efficiency of language pre-training methods through providing better data utilization. It is well-known that in language data corpus, words follow ... | TAKING NOTES ON THE FLY HELPS LANGUAGE PRE-TRAINING |
d247476419 | Training neural networks requires increasing amounts of memory. Parameter sharing can reduce memory and communication costs, but existing methods assume networks have many identical layers and utilize hand-crafted sharing strategies that fail to generalize. We introduce Neural Parameter Allocation Search (NPAS), a nove... | NEURAL PARAMETER ALLOCATION SEARCH |
d247597138 | Modern language models can generate high-quality short texts. However, they often meander or are incoherent when generating longer texts. These issues arise from the next-token-only language modeling objective. Recent work in selfsupervised learning suggests that models can learn good latent representations via contras... | LANGUAGE MODELING VIA STOCHASTIC PROCESSES |
d252683988 | Conditional language models are predominantly trained with maximum likelihood estimation (MLE), giving probability mass to sparsely observed target sequences. While MLE trained models assign high probability to plausible sequences given the context, the model probabilities often do not accurately rank-order generated s... | CALIBRATING SEQUENCE LIKELIHOOD IMPROVES CONDITIONAL LANGUAGE GENERATION |
d263608698 | The video-language (VL) pretraining has achieved remarkable improvement in multiple downstream tasks.However, the current VL pretraining framework is hard to extend to multiple modalities (N modalities, N ≥ 3) beyond vision and language.We thus propose LanguageBind, taking the language as the bind across different moda... | LANGUAGEBIND: EXTENDING VIDEO-LANGUAGE PRETRAINING TO N-MODALITY BY LANGUAGE-BASED SEMANTIC ALIGNMENT |
d258887457 | We formalize and study a phenomenon called feature collapse that makes precise the intuitive idea that entities playing a similar role in a learning task receive similar representations. As feature collapse requires a notion of task, we leverage a simple but prototypical NLP task to study it. We start by showing experi... | Feature Collapse |
d247595075 | Many approaches to program synthesis perform a search within an enormous space of programs to find one that satisfies a given specification. Prior works have used neural models to guide combinatorial search algorithms, but such approaches still explore a huge portion of the search space and quickly become intractable a... | CROSSBEAM: LEARNING TO SEARCH IN BOTTOM-UP PROGRAM SYNTHESIS |
d245877810 | Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions that may cause performance drops. In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data. We propose Aver... | LEVERAGING UNLABELED DATA TO PREDICT OUT-OF-DISTRIBUTION PERFORMANCE |
d245769552 | Video recordings of speech contain correlated audio and visual information, providing a strong signal for speech representation learning from the speaker's lip movements and the produced sound. We introduce Audio-Visual Hidden Unit BERT (AV-HuBERT), a self-supervised representation learning framework for audio-visual s... | LEARNING AUDIO-VISUAL SPEECH REPRESENTATION BY MASKED MULTIMODAL CLUSTER PREDICTION |
d222290737 | Federated learning is typically approached as an optimization problem, where the goal is to minimize a global loss function by distributing computation across client devices that possess local data and specify different parts of the global objective. We present an alternative perspective and formulate federated learnin... | Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms |
d252967802 | Visualization methods based on the nearest neighbor graph, such as t-SNE or UMAP, are widely used for visualizing high-dimensional data. Yet, these approaches only produce meaningful results if the nearest neighbors themselves are meaningful. For images represented in pixel space this is not the case, as distances in p... | UNSUPERVISED VISUALIZATION OF IMAGE DATASETS USING CONTRASTIVE LEARNING |
d52877454 | We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner. DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of graphs-both derived using established graph convolutional ... | DEEP GRAPH INFOMAX |
d255340736 | We propose a simple data model inspired from natural data such as text or images, and use it to study the importance of learning features in order to achieve good generalization. Our data model follows a long-tailed distribution in the sense that some rare subcategories have few representatives in the training set. In ... | Long-Tailed Learning Requires Feature Learning |
d209832425 | Despite the growing interest in continual learning, most of its contemporary works have been studied in a rather restricted setting where tasks are clearly distinguishable, and task boundaries are known during training. However, if our goal is to develop an algorithm that learns as humans do, this setting is far from r... | A NEURAL DIRICHLET PROCESS MIXTURE MODEL FOR TASK-FREE CONTINUAL LEARNING |
d13019454 | Existing sequence prediction methods are mostly concerned with time-independent sequences, in which the actual time span between events is irrelevant and the distance between events is simply the difference between their order positions in the sequence. While this time-independent view of sequences is applicable for da... | Time-Dependent Representation for Neural Event Sequence Prediction |
d259341735 | We present SDXL, a latent diffusion model for text-to-image synthesis. Compared to previous versions of Stable Diffusion, SDXL leverages a three times larger UNet backbone: The increase of model parameters is mainly due to more attention blocks and a larger cross-attention context as SDXL uses a second text encoder. We... | SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis |
d251018190 | Many point-based 3D detectors adopt point-feature sampling strategies to drop some points for efficient inference. These strategies are typically based on fixed and handcrafted rules, making it difficult to handle complicated scenes. Different from them, we propose a Dynamic Ball Query (DBQ) network to adaptively selec... | DBQ-SSD: DYNAMIC BALL QUERY FOR EFFICIENT 3D OBJECT DETECTION |
d61153617 | State-of-the-art models are now trained with billions of parameters, reaching hardware limits in terms of memory consumption. This has created a recent demand for memory-efficient optimizers. To this end, we investigate the limits and performance tradeoffs of memory-efficient adaptively preconditioned gradient methods.... | Extreme Tensoring for Low-Memory Preconditioning |
d53402824 | Counterfactual Regret Minimization (CRF) is a fundamental and effective technique for solving Imperfect Information Games (IIG). However, the original CRF algorithm only works for discrete state and action spaces, and the resulting strategy is maintained as a tabular representation. Such tabular representation limits t... | Double Neural Counterfactual Regret Minimization |
d258999337 | During interactive segmentation, a model and a user work together to delineate objects of interest in a 3D point cloud.In an iterative process, the model assigns each data point to an object (or the background), while the user corrects errors in the resulting segmentation and feeds them back into the model.The current ... | AGILE3D: ATTENTION GUIDED INTERACTIVE MULTI-OBJECT 3D SEGMENTATION |
d225076227 | Selective classification, in which models are allowed to abstain on uncertain predictions, is a natural approach to improving accuracy in settings where errors are costly but abstentions are manageable. In this paper, we find that while selective classification can improve average accuracies, it can simultaneously magn... | SELECTIVE CLASSIFICATION CAN MAGNIFY DISPARITIES ACROSS GROUPS |
d4429876 | We study the error landscape of deep linear and nonlinear neural networks with square error loss. We build on the recent results in the literature and present necessary and sufficient conditions for a critical point of the empirical risk function to be a global minimum in the deep linear network case. Our simple condit... | Global optimality conditions for deep neural networks |
d209501050 | A white noise analysis of modern deep neural networks is presented to unveil their biases at the whole network level or the single neuron level. Our analysis is based on two popular and related methods in psychophysics and neurophysiology namely classification images and spike triggered analysis. These methods have bee... | WHITE NOISE ANALYSIS OF NEURAL NETWORKS |
d202749930 | Program verification offers a framework for ensuring program correctness and therefore systematically eliminating different classes of bugs. Inferring loop invariants is one of the main challenges behind automated verification of real-world programs which often contain many loops. In this paper, we present Continuous L... | CLN2INV: LEARNING LOOP INVARIANTS WITH CONTINUOUS LOGIC NETWORKS |
d3292002 | We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over thei... | GRAPH ATTENTION NETWORKS |
d202888950 | Few-shot classification is the task of predicting the category of an example from few labeled examples. The number of labeled examples per category is called the number of shots (or shot number). Recent works tackle this task through metalearning, where a meta-learner extracts information from observed tasks during met... | A THEORETICAL ANALYSIS OF THE NUMBER OF SHOTS IN FEW-SHOT LEARNING |
d91175758 | For many evaluation metrics commonly used as benchmarks for unconditional image generation, trivially memorizing the training set attains a better score than models which are considered state-of-the-art; we consider this problematic. We clarify a necessary condition for an evaluation metric not to behave this way: esti... | TOWARDS GAN BENCHMARKS WHICH REQUIRE GENERALIZATION |
d257405483 | Hyperparameter optimization (HPO) and neural architecture search (NAS) are methods of choice to obtain the best-in-class machine learning models, but in practice they can be costly to run. When models are trained on large datasets, tuning them with HPO or NAS rapidly becomes prohibitively expensive for practitioners, e... | PASHA: EFFICIENT HPO AND NAS WITH PROGRESSIVE RESOURCE ALLOCATION |
d262828485 | Maintaining legacy software requires many software and systems engineering hours.Assembly code programs, which demand low-level control over the computer machine state and have no variable names, are particularly difficult for humans to analyze.Existing conventional program translators guarantee correctness, but are ha... | GUESS & SKETCH: LANGUAGE MODEL GUIDED TRANSPILATION |
d248392030 | We provide sharp path-dependent generalization and excess risk guarantees for the full-batch Gradient Descent (GD) algorithm on smooth losses (possibly non-Lipschitz, possibly nonconvex). At the heart of our analysis is an upper bound on the generalization error, which implies that average output stability and a bounde... | Beyond Lipschitz: Sharp Generalization and Excess Risk Bounds for Full-Batch GD |
d204905143 | Generative Adversarial Networks (GANs) are known to be difficult to train, despite considerable research effort. Several regularization techniques for stabilizing training have been proposed, but they introduce non-trivial computational overheads and interact poorly with existing techniques like spectral normalization.... | CONSISTENCY REGULARIZATION FOR GENERATIVE ADVERSARIAL NETWORKS |
d52909341 | We study the problem of representation learning in goal-conditioned hierarchical reinforcement learning. In such hierarchical structures, a higher-level controller solves tasks by iteratively communicating goals which a lower-level policy is trained to reach. Accordingly, the choice of representation -the mapping of ob... | NEAR-OPTIMAL REPRESENTATION LEARNING FOR HIERARCHICAL REINFORCEMENT LEARNING |
d252531820 | Recent approximations to backpropagation (BP) have mitigated many of BP's computational inefficiencies and incompatibilities with biology, but important limitations still remain. Moreover, the approximations significantly decrease accuracy in benchmarks, suggesting that an entirely different approach may be more fruitf... | Hebbian Deep Learning Without Feedback |
d85449634 | This paper introduces a new framework for open-domain question answering in which the retriever and the reader iteratively interact with each other. The framework is agnostic to the architecture of the machine reading model, only requiring access to the token-level hidden representations of the reader. The retriever us... | MULTI-STEP RETRIEVER-READER INTERACTION FOR SCALABLE OPEN-DOMAIN QUESTION ANSWERING |
d11324902 | The learning of domain-invariant representations in the context of domain adaptation with neural networks is considered. We propose a new regularization method that minimizes the domain-specific latent feature representations directly in the hidden activation space. Although some standard distribution matching approach... | CENTRAL MOMENT DISCREPANCY (CMD) FOR DOMAIN-INVARIANT REPRESENTATION LEARNING |
d57759353 | Human perception of 3D shapes goes beyond reconstructing them as a set of points or a composition of geometric primitives: we also effortlessly understand higherlevel shape structure such as the repetition and reflective symmetry of object parts. In contrast, recent advances in 3D shape sensing focus more on low-level ... | LEARNING TO INFER AND EXECUTE 3D SHAPE PROGRAMS |
d263605565 | Image denoisers have been shown to be powerful priors for solving inverse problems in imaging.In this work, we introduce a generalization of these methods that allows any image restoration network to be used as an implicit prior.The proposed method uses priors specified by deep neural networks pre-trained as general re... | A Restoration Network as an Implicit Prior |
d235368204 | Researchers are using deep learning models to explore the emergence of language in various language games, where agents interact and develop an emergent language to solve tasks. We focus on the factors that determine the expressivity of emergent languages, which reflects the amount of information about input spaces tho... | EXPRESSIVITY OF EMERGENT LANGUAGES IS A TRADE-OFF BETWEEN CONTEXTUAL COMPLEXITY AND UNPREDICTABILITY |
d236318292 | Irregularly sampled time series commonly occur in several domains where they present a significant challenge to standard deep learning models. In this paper, we propose a new deep learning framework for probabilistic interpolation of irregularly sampled time series that we call the Heteroscedastic Temporal Variational ... | Heteroscedastic Temporal Variational Autoencoder For Irregularly Sampled Time Series |
d253180351 | Training machine learning models robust to distribution shifts is critical for real-world applications.Some robust training algorithms (e.g., Group DRO) specialize to group shifts and require group information on all training points.Other methods (e.g., CVaR DRO) that do not need group annotations can be overly conserv... | BITRATE-CONSTRAINED DRO: BEYOND WORST CASE ROBUSTNESS TO UNKNOWN GROUP SHIFTS |
d52903499 | We provide a theoretical algorithm for checking local optimality and escaping saddles at nondifferentiable points of empirical risks of two-layer ReLU networks. Our algorithm receives any parameter value and returns: local minimum, second-order stationary point, or a strict descent direction. The presence of M data poi... | Efficiently testing local optimality and escaping saddles for ReLU networks |
d263829697 | Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities.We consider real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models.We the... | SWE-BENCH: CAN LANGUAGE MODELS RESOLVE REAL-WORLD GITHUB ISSUES? |
d53215593 | This paper investigates whether learning contingency-awareness and controllable aspects of an environment can lead to better exploration in reinforcement learning. To investigate this question, we consider an instantiation of this hypothesis evaluated on the Arcade Learning Element (ALE). In this study, we develop an a... | CONTINGENCY-AWARE EXPLORATION IN REINFORCEMENT LEARNING |
d220347587 | Deep one-class classification variants for anomaly detection learn a mapping that concentrates nominal samples in feature space causing anomalies to be mapped away. Because this transformation is highly non-linear, finding interpretations poses a significant challenge. In this paper we present an explainable deep one-c... | Explainable Deep One-Class Classification |
d8768364 | Unsupervised learning of probabilistic models is a central yet challenging problem in machine learning. Specifically, designing models with tractable learning, sampling, inference and evaluation is crucial in solving this task. We extend the space of such models using real-valued non-volume preserving (real NVP) transf... | Density estimation using Real NVP |
d264128269 | Implementing a reward function that perfectly captures a complex task in the real world is impractical.As a result, it is often appropriate to think of the reward function as a proxy for the true objective rather than as its definition.We study this phenomenon through the lens of Goodhart's law, which predicts that inc... | GOODHART'S LAW IN REINFORCEMENT LEARNING |
d257482747 | We present MovingParts, a NeRF-based method for dynamic scene reconstruction and part discovery. We consider motion as an important cue for identifying parts, that all particles on the same part share the common motion pattern. From the perspective of fluid simulation, existing deformation-based methods for dynamic NeR... | MovingParts: Motion-based 3D Part Discovery in Dynamic Radiance Field |
d247476256 | Boundaries are among the primary visual cues used by human and computer vision systems. One of the key problems in boundary detection is the label representation, which typically leads to class imbalance and, as a consequence, to thick boundaries that require non-differential post-processing steps to be thinned. In thi... | ZERO PIXEL DIRECTIONAL BOUNDARY BY VECTOR TRANSFORM |
d220055921 | A hallmark of human intelligence is the ability to interact directly with raw data and acquire rich, general-purpose conceptual representations. In machine learning, symbolic models can capture the compositional and causal knowledge that enables flexible generalization, but they struggle to learn from raw inputs, relyi... | Learning Task-General Representations with Generative Neuro-Symbolic Modeling |
d52901777 | We consider the problem of uncertainty estimation in the context of (non-Bayesian) deep neural classification. In this context, all known methods are based on extracting uncertainty signals from a trained network optimized to solve the classification problem at hand. We demonstrate that such techniques tend to introduc... | Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers |
d202712898 | Neural architecture search (NAS) searches architectures automatically for given tasks, e.g., image classification and language modeling. Improving the search efficiency and effectiveness have attracted increasing attention in recent years. However, few efforts have been devoted to understanding the generated architectu... | UNDERSTANDING ARCHITECTURES LEARNT BY CELL-BASED NEURAL ARCHITECTURE SEARCH |
d3458474 | We propose a principled method for kernel learning, which relies on a Fourier-analytic characterization of translation-invariant or rotation-invariant kernels. Our method produces a sequence of feature maps, iteratively refining the SVM margin. We provide rigorous guarantees for optimality and generalization, interpret... | Not-So-Random Features |
d13807351 | This paper proposes a new optimization algorithm called Entropy-SGD for training deep neural networks that is motivated by the local geometry of the energy landscape. Local extrema with low generalization error have a large proportion of almost-zero eigenvalues in the Hessian with very few positive or negative eigenval... | ENTROPY-SGD: BIASING GRADIENT DESCENT INTO WIDE VALLEYS |
d3470596 | At present, the vast majority of building blocks, techniques, and architectures for deep learning are based on real-valued operations and representations. However, recent work on recurrent neural networks and older fundamental theoretical analysis suggests that complex numbers could have a richer representational capac... | Deep Complex Networks |
d222066778 | Deep networks are often considered to be more expressive than shallow ones in terms of approximation. Indeed, certain functions can be approximated by deep networks provably more efficiently than by shallow ones, however, no tractable algorithms are known for learning such deep models. Separately, a recent line of work... | Deep Equals Shallow for ReLU Networks in Kernel Regimes |
d250243645 | Machine learning models exhibit two seemingly contradictory phenomena: training data memorization, and various forms of forgetting. In memorization, models overfit specific training examples and become susceptible to privacy attacks. In forgetting, examples which appeared early in training are forgotten by the end. In ... | Measuring Forgetting of Memorized Training Examples |
d52948669 | Recently mean field theory has been successfully used to analyze properties of wide, random neural networks. It has given rise to a prescriptive theory for initializing neural networks, which ensures that the 2 norm of the backpropagated gradients is bounded, and training is orders of magnitude faster. Despite the stro... | Information Geometry of Orthogonal Initializations and Training |
d249848252 | Evaluation metrics in image synthesis play a key role to measure performances of generative models. However, most metrics mainly focus on image fidelity. Existing diversity metrics are derived by comparing distributions, and thus they cannot quantify the diversity or rarity degree of each generated image. In this work,... | Rarity Score : A New Metric to Evaluate the Uncommonness of Synthesized Images |
d222125236 | We introduce k-nearest-neighbor machine translation (kNN-MT), which predicts tokens with a nearest neighbor classifier over a large datastore of cached examples, using representations from a neural translation model for similarity search.This approach requires no additional training and scales to give the decoder direc... | NEAREST NEIGHBOR MACHINE TRANSLATION |
d85543148 | A well-trained model should classify objects with a unanimous score for every category. This requires the high-level semantic features should be as much alike as possible among samples. To achive this, previous works focus on re-designing the loss or proposing new regularization constraints. In this paper, we provide a... | FEATURE INTERTWINER FOR OBJECT DETECTION |
d203837733 | We study the problem of learning associative memory -a system which is able to retrieve a remembered pattern based on its distorted or incomplete version. Attractor networks provide a sound model of associative memory: patterns are stored as attractors of the network dynamics and associative retrieval is performed by r... | META-LEARNING DEEP ENERGY-BASED MEMORY MODELS |
d251953412 | In large-scale retrieval, the lexicon-weighting paradigm, learning weighted sparse representations in vocabulary space, has shown promising results with high quality and low latency. Despite it deeply exploiting the lexicon-representing capability of pre-trained language models, a crucial gap remains between language m... | LEXMAE: LEXICON-BOTTLENECKED PRETRAINING FOR LARGE-SCALE RETRIEVAL |
d257079136 | Offline reinforcement learning (RL) is a challenging setting where existing offpolicy actor-critic methods perform poorly due to the overestimation of out-ofdistribution state-action pairs. Thus, various additional augmentations are proposed to keep the learned policy close to the offline dataset (or the behavior polic... | BEHAVIOR PROXIMAL POLICY OPTIMIZATION |
d262065523 | Safe deployment of graph neural networks (GNNs) under distribution shift requires models to provide accurate confidence indicators (CI).However, while it is wellknown in computer vision that CI quality diminishes under distribution shift, this behavior remains understudied for GNNs.Hence, we begin with a case study on ... | ACCURATE AND SCALABLE ESTIMATION OF EPISTEMIC UNCERTAINTY FOR GRAPH NEURAL NETWORKS |
d3473900 | By representing words with probability densities rather than point vectors, probabilistic word embeddings can capture rich and interpretable semantic information and uncertainty. The uncertainty information can be particularly meaningful in capturing entailment relationships -whereby general words such as "entity" corr... | HIERARCHICAL DENSITY ORDER EMBEDDINGS |
d253117068 | Discovering causal relationships between different variables from time series data has been a long-standing challenge for many domains such as climate science, finance and healthcare. Given the the complexity of real-world relationships and the nature of observations in discrete time, causal discovery methods need to c... | RHINO: DEEP CAUSAL TEMPORAL RELATIONSHIP LEARNING WITH HISTORY-DEPENDENT NOISE |
d225103201 | Empirical studies suggest that machine learning models often rely on features, such as the background, that may be spuriously correlated with the label only during training time, resulting in poor accuracy during test-time. In this work, we identify the fundamental factors that give rise to this behavior, by explaining... | Understanding the Failure Modes of Out-of-Distribution Generalization |
d261395800 | Retrosynthesis planning is a fundamental challenge in chemistry which aims at designing reaction pathways from commercially available starting materials to a target molecule. Each step in multi-step retrosynthesis planning requires accurate prediction of possible precursor molecules given the target molecule and confid... | RETROBRIDGE: MODELING RETROSYNTHESIS WITH MARKOV BRIDGES |
d212874725 | Off-policy estimation for long-horizon problems is important in many real-life applications such as healthcare and robotics, where high-fidelity simulators may not be available and on-policy evaluation is expensive or impossible. Recently,[21]proposed an approach that avoids the curse of horizon suffered by typical imp... | Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning |
d209319223 | An important problem that arises in reinforcement learning and Monte Carlo methods is estimating quantities defined by the stationary distribution of a Markov chain. In many real-world applications, access to the underlying transition operator is limited to a fixed set of data that has already been collected, without a... | GENDICE: GENERALIZED OFFLINE ESTIMATION OF STATIONARY VALUES |
d229188065 | While federated learning traditionally aims to train a single global model across decentralized local datasets, one model may not always be ideal for all participating clients. Here we propose an alternative, where each client only federates with other relevant clients to obtain a stronger model per client-specific obj... | Personalized Federated Learning with First Order Model Optimization |
d263152476 | Large Language Models (LLMs) have recently demonstrated a remarkable success across various tasks.However, efficiently serving LLMs has been a challenge due to its large memory bottleneck, specifically in small batch inference settings (e.g.mobile devices).Weight-only quantization can be a promising approach, but sub-4... | RETHINKING CHANNEL DIMENSIONS TO ISOLATE OUTLIERS FOR LOW-BIT WEIGHT QUANTIZATION OF LARGE LANGUAGE MODELS |
d263620583 | In order to understand the in-context learning phenomenon, recent works have adopted a stylized experimental framework and demonstrated that Transformers can learn gradient-based learning algorithms for various classes of real-valued functions.However, the limitations of Transformers in implementing learning algorithms... | UNDERSTANDING IN-CONTEXT LEARNING IN TRANSFORMERS AND LLMS BY LEARNING TO LEARN DISCRETE FUNCTIONS |
d53483414 | Deep neural networks have been shown to perform well in many classical machine learning problems, especially in image classification tasks. However, researchers have found that neural networks can be easily fooled, and they are surprisingly sensitive to small perturbations imperceptible to humans. Carefully crafted inp... | A DIRECT APPROACH TO ROBUST DEEP LEARNING USING ADVERSARIAL NETWORKS |
d251648079 | Deep neural networks are used for a wide range of regression problems. However, there exists a significant gap in accuracy between specialized approaches and generic direct regression in which a network is trained by minimizing the squared or absolute error of output labels. Prior work has shown that solving a regressi... | LABEL ENCODING FOR REGRESSION NETWORKS |
d53208122 | Traditional natural language generation (NLG) models are trained using maximum likelihood estimation (MLE) which differs from the sample generation inference procedure. During training the ground truth tokens are passed to the model, however, during inference, the model instead reads its previously generated samples -a... | LANGUAGE GANS FALLING SHORT |
d221139554 | Dense Associative Memories or modern Hopfield networks permit storage and reliable retrieval of an exponentially large (in the dimension of feature space) number of memories. At the same time, their naive implementation is non-biological, since it seemingly requires the existence of many-body synaptic junctions between... | Large Associative Memory Problem in Neurobiology and Machine Learning |
d220265948 | We propose a new probabilistic method for unsupervised recovery of corrupted data. Given a large ensemble of degraded samples, our method recovers accurate posteriors of clean values, allowing the exploration of the manifold of possible reconstructed data and hence characterising the underlying uncertainty. In this set... | Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data |
d51942590 | We study instancewise feature importance scoring as a method for model interpretation. Any such method yields, for each predicted instance, a vector of importance scores associated with the feature vector. Methods based on the Shapley score have been proposed as a fair way of computing feature attributions of this kind... | L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data |
d212633677 | We show that a deep neural network can learn the semantics of linear-time temporal logic (LTL). As a challenging task that requires deep understanding of the LTL semantics, we show that our network can solve the trace generation problem for LTL: given a satisfiable LTL formula, find a trace that satisfies the formula. ... | Teaching Temporal Logics to Neural Networks |
d219530873 | When thrust into an unfamiliar environment and charged with solving a series of tasks, an effective agent should (1) leverage prior knowledge to solve its current task while (2) efficiently exploring to gather knowledge for use in future tasks, and then (3) plan using that knowledge when faced with new tasks in that sa... | Rapid Task-Solving in Novel Environments |
d249375359 | While the empirical success of self-supervised learning (SSL) heavily relies on the usage of deep nonlinear models, existing theoretical works on SSL understanding still focus on linear ones. In this paper, we study the role of nonlinearity in the training dynamics of contrastive learning (CL) on one and two-layer nonl... | UNDERSTANDING THE ROLE OF NONLINEARITY IN TRAINING DYNAMICS OF CONTRASTIVE LEARNING |
d257365130 | Deep neural networks based on layer-stacking architectures have historically suffered from poor inherent interpretability. Meanwhile, symbolic probabilistic models function with clear interpretability, but how to combine them with neural networks to enhance their performance remains to be explored. In this paper, we tr... | A MULTI-GRAINED SELF-INTERPRETABLE SYMBOLIC-NEURAL MODEL FOR SINGLE/MULTI-LABELED TEXT CLASSIFICATION |
d244117004 | Protein complex formation is a central problem in biology, being involved in most of the cell's processes, and essential for applications, e.g. drug design or protein engineering. We tackle rigid body protein-protein docking, i.e., computationally predicting the 3D structure of a protein-protein complex from the indivi... | INDEPENDENT SE(3)-EQUIVARIANT MODELS FOR END-TO-END RIGID PROTEIN DOCKING |
d233033761 | Training on synthetic data can be beneficial for label or data-scarce scenarios. However, synthetically trained models often suffer from poor generalization in real domains due to domain gaps. In this work, we make a key observation that the diversity of the learned feature embeddings plays an important role in the gen... | CONTRASTIVE SYN-TO-REAL GENERALIZATION |
d235367997 | For real-time forecasting in domains like public health and macroeconomics, data collection is a non-trivial and demanding task. Often after being initially released, it undergoes several revisions later (maybe due to human or technical constraints) -as a result, it may take weeks until the data reaches a stable value.... | BACK2FUTURE: LEVERAGING BACKFILL DYNAMICS FOR IMPROVING REAL-TIME PREDICTIONS IN FUTURE |
d220514300 | Lifting is an efficient technique to scale up graphical models generalized to relational domains by exploiting the underlying symmetries. Concurrently, neural models are continuously expanding from grid-like tensor data into structured representations, such as various attributed graphs and relational databases. To addr... | Lossless Compression of Structured Convolutional Models via Lifting |
d263672117 | Characterizing the relationship between neural population activity and behavioral data is a central goal of neuroscience.While latent variable models (LVMs) are successful in describing high-dimensional time-series data, they are typically only designed for a single type of data, making it difficult to identify structu... | Multi-modal Gaussian Process Variational Autoencoders for Neural and Behavioral Data |
d258298703 | The recently proposed data augmentation TransMix employs attention labels to help visual transformers (ViT) achieve better robustness and performance. However, TransMix is deficient in two aspects: 1) The image cropping method of TransMix may not be suitable for ViTs. 2) At the early stage of training, the model produc... | MIXPRO: DATA AUGMENTATION WITH MASKMIX AND PROGRESSIVE ATTENTION LABELING FOR VISION TRANSFORMER |
d4117071 | Effective training of neural networks requires much data. In the low-data regime, parameters are underdetermined, and learnt networks generalise poorly. Data Augmentation(Krizhevsky et al., 2012)alleviates this by using existing data more effectively. However standard data augmentation produces only limited plausible a... | DATA AUGMENTATION GENERATIVE ADVERSARIAL NETWORKS |
d252780488 | In this work, we explore the maximum-margin bias of quasi-homogeneous neural networks trained with gradient flow on an exponential loss and past a point of separability. We introduce the class of quasi-homogeneous models, which is expressive enough to describe nearly all neural networks with homogeneous activations, ev... | THE ASYMMETRIC MAXIMUM MARGIN BIAS OF QUASI-HOMOGENEOUS NEURAL NETWORKS |
d255749563 | The key to high-level cognition is believed to be the ability to systematically manipulate and compose knowledge pieces. While token-like structured knowledge representations are naturally provided in text, it is elusive how to obtain them for unstructured modalities such as scene images. In this paper, we propose a ne... | NEURAL SYSTEMATIC BINDER |
d257757379 | Most approaches for self-supervised learning (SSL) are optimised on curated balanced datasets, e.g. ImageNet, despite the fact that natural data usually exhibits long-tail distributions. In this paper, we analyse the behaviour of one of the most popular variants of SSL, i.e. contrastive methods, on long-tail data. In p... | TEMPERATURE SCHEDULES FOR SELF-SUPERVISED CONTRASTIVE METHODS ON LONG-TAIL DATA |
d36060542 | Learning-based representations have become the defacto means to address computer vision tasks. Despite their massive adoption, the amount of work aiming at understanding the internal representations learned by these models is rather limited. Existing methods aimed at model interpretation either require exhaustive manua... | Visual Explanation by Interpretation: Improving Visual Feedback Capabilities of Deep Neural Networks |
d263609211 | Few neural architectures lend themselves to provable learning with gradient based methods.One popular model is the single-index model, in which labels are produced by composing an unknown linear projection with a possibly unknown scalar link function.Learning this model with SGD is relatively well-understood, whereby t... | Symmetric Single Index Learning |
d263829260 | We study the problem of online prediction, in which at each time step t ∈ {1, 2, · · · T }, an individual xt arrives, whose label we must predict. Each individual is associated with various groups, defined based on their features such as age, sex, race etc., which may intersect. Our goal is to make predictions that hav... | Oracle Efficient Algorithms for Groupwise Regret |
d3503217 | Ability to continuously learn and adapt from limited experience in nonstationary environments is an important milestone on the path towards general intelligence. In this paper, we cast the problem of continuous adaptation into the learning-to-learn framework. We develop a simple gradient-based meta-learning algorithm s... | Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments |
d22163777 | This paper proposes a new actor-critic-style algorithm called Dual Actor-Criticor Dual-AC. It is derived in a principled way from the Lagrangian dual form of the Bellman optimality equation, which can be viewed as a two-player game between the actor and a critic-like function, which is named as dual critic. Compared to... | Boosting the Actor with Dual Critic |
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