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d246294808 | As one of the most fundamental stochastic optimization algorithms, stochastic gradient descent (SGD) has been intensively developed and extensively applied in machine learning in the past decade. There have been some modified SGD-type algorithms, which outperform the SGD in many competitions and applications in terms o... | ON THE CONVERGENCE OF MSGD AND ADAGRAD FOR STOCHASTIC OPTIMIZATION |
d238215654 | Understanding the source of the superior generalization ability of NNs remains one of the most important problems in ML research. There have been a series of theoretical works trying to derive non-vacuous bounds for NNs. Recently, the compression of information stored in weights (IIW) is proved to play a key role in NN... | PAC-BAYES INFORMATION BOTTLENECK |
d260926651 | 1 Communication compression, a technique aiming to reduce the information volume to be transmitted over the air, has gained great interests in Federated Learning (FL) for the potential of alleviating its communication overhead. However, communication compression brings forth new challenges in FL due to the interplay of... | Stochastic Controlled Averaging for Federated Learning with Communication Compression |
d239009958 | When designing Convolutional Neural Networks (CNNs), one must select the size of the convolutional kernels before training. Recent works show CNNs benefit from different kernel sizes at different layers, but exploring all possible combinations is unfeasible in practice. A more efficient approach is to learn the kernel ... | FLEXCONV: CONTINUOUS KERNEL CONVOLUTIONS WITH DIFFERENTIABLE KERNEL SIZES |
d213692365 | Designing a convolution for a spherical neural network requires a delicate tradeoff between efficiency and rotation equivariance. DeepSphere, a method based on a graph representation of the sampled sphere, strikes a controllable balance between these two desiderata. This contribution is twofold. First, we study both th... | DEEPSPHERE: A GRAPH-BASED SPHERICAL CNN |
d258987795 | Bayesian optimization is a highly efficient approach to optimizing objective functions which are expensive to query. These objectives are typically represented by Gaussian process (GP) surrogate models which are easy to optimize and support exact inference. While standard GP surrogates have been well-established in Bay... | A Study of Bayesian Neural Network Surrogates for Bayesian Optimization |
d221970302 | We propose a simple and efficient multi-hop dense retrieval approach for answering complex open-domain questions, which achieves state-of-the-art performance on two multi-hop datasets, HotpotQA and multi-evidence FEVER. Contrary to previous work, our method does not require access to any corpus-specific information, su... | ANSWERING COMPLEX OPEN-DOMAIN QUESTIONS WITH MULTI-HOP DENSE RETRIEVAL |
d263909278 | Large transformer models pretrained on offline reinforcement learning datasets have demonstrated remarkable in-context reinforcement learning (ICRL) capabilities, where they can make good decisions when prompted with interaction trajectories from unseen environments. However, when and how transformers can be trained to... | Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining |
d247245054 | Model-agnostic meta-learning (MAML) is one of the most popular and widely adopted meta-learning algorithms, achieving remarkable success in various learning problems. Yet, with the unique design of nested inner-loop and outer-loop updates, which govern the task-specific and meta-model-centric learning, respectively, th... | MAML IS A NOISY CONTRASTIVE LEARNER IN CLASSIFICATION |
d247518540 | A fundamental question in adversarial machine learning is whether a robust classifier exists for a given task. A line of research has made some progress towards this goal by studying the concentration of measure, but we argue standard concentration fails to fully characterize the intrinsic robustness of a classificatio... | UNDERSTANDING INTRINSIC ROBUSTNESS USING LABEL UNCERTAINTY |
d257365083 | A core component of human intelligence is the ability to identify abstract patterns inherent in complex, high-dimensional perceptual data, as exemplified by visual reasoning tasks such as Raven's Progressive Matrices (RPM).Motivated by the goal of designing AI systems with this capacity, recent work has focused on eval... | LEARNING TO REASON OVER VISUAL OBJECTS |
d195584474 | Reinforcement learning agents that operate in diverse and complex environments can benefit from the structured decomposition of their behavior. Often, this is addressed in the context of hierarchical reinforcement learning, where the aim is to decompose a policy into lower-level primitives or options, and a higher-leve... | Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives |
d257771678 | How well do reward functions learned with inverse reinforcement learning (IRL) generalize? We illustrate that state-of-the-art IRL algorithms, which maximize a maximum-entropy objective, learn rewards that overfit to the demonstrations. Such rewards struggle to provide meaningful rewards for states not covered by the d... | BC-IRL: LEARNING GENERALIZABLE REWARD FUNCTIONS FROM DEMONSTRATIONS |
d245064979 | This paper tackles the problem of learning value functions from undirected stateonly experience (state transitions without action labels i.e. (s, s , r) tuples). We first theoretically characterize the applicability of Q-learning in this setting. We show that tabular Q-learning in discrete Markov decision processes (MD... | LEARNING VALUE FUNCTIONS FROM UNDIRECTED STATE-ONLY EXPERIENCE |
d246867225 | Anomaly detection is a widely studied task for a broad variety of data types; among them, multiple time series appear frequently in applications, including for example, power grids and traffic networks. Detecting anomalies for multiple time series, however, is a challenging subject, owing to the intricate interdependen... | GRAPH-AUGMENTED NORMALIZING FLOWS FOR ANOMALY DETECTION OF MULTIPLE TIME SERIES |
d224818149 | 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 propos... | PROBABILISTIC NUMERIC CONVOLUTIONAL NEURAL NETWORKS |
d253581567 | 3D object detection from multiple image views is a fundamental and challenging task for visual scene understanding. Owing to its low cost and high efficiency, multi-view 3D object detection has demonstrated promising application prospects. However, accurately detecting objects through perspective views is extremely dif... | BEVDISTILL: CROSS-MODAL BEV DISTILLATION FOR MULTI-VIEW 3D OBJECT DETECTION |
d250048634 | Despite their widespread success in various domains, Transformer networks have yet to perform well across datasets in the domain of 3D atomistic graphs such as molecules even when 3D-related inductive biases like translational invariance and rotational equivariance are considered. In this paper, we demonstrate that Tra... | EQUIFORMER: EQUIVARIANT GRAPH ATTENTION TRANSFORMER FOR 3D ATOMISTIC GRAPHS |
d76649575 | Many real world tasks exhibit rich structure that is repeated across different parts of the state space or in time. In this work we study the possibility of leveraging such repeated structure to speed up and regularize learning. We start from the KL regularized expected reward objective which introduces an additional c... | INFORMATION ASYMMETRY IN KL-REGULARIZED RL |
d239998500 | Learning rate schedulers have been widely adopted in training deep neural networks. Despite their practical importance, there is a discrepancy between its practice and its theoretical analysis. For instance, it is not known what schedules of SGD achieve best convergence, even for simple problems such as optimizing quad... | EIGENCURVE: OPTIMAL LEARNING RATE SCHEDULE FOR SGD ON QUADRATIC OBJECTIVES WITH SKEWED HESSIAN SPECTRUMS |
d234470177 | We study the problem of learning Bayesian networks where an ǫ-fraction of the samples are adversarially corrupted. We focus on the fully-observable case where the underlying graph structure is known. In this work, we present the first nearly-linear time algorithm for this problem with a dimension-independent error guar... | Robust Learning of Fixed-Structure Bayesian Networks in Nearly-Linear Time |
d173188788 | We consider the problem of unconstrained minimization of a smooth objective function in R d in setting where only function evaluations are possible. We propose and analyze stochastic zeroth-order method with heavy ball momentum. In particular, we propose, SMTP, a momentum version of the stochastic three-point method (S... | A STOCHASTIC DERIVATIVE FREE OPTIMIZATION METHOD WITH MOMENTUM |
d195317051 | Neural networks powered with external memory simulate computer behaviors. These models, which use the memory to store data for a neural controller, can learn algorithms and other complex tasks. In this paper, we introduce a new memory to store weights for the controller, analogous to the stored-program memory in modern... | Neural Stored-program Memory |
d59291917 | We propose a simple yet highly effective method that addresses the mode-collapse problem in the Conditional Generative Adversarial Network (cGAN). Although conditional distributions are multi-modal (i.e., having many modes) in practice, most cGAN approaches tend to learn an overly simplified distribution where an input... | DIVERSITY-SENSITIVE CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS |
d5031534 | While deep neural networks have proven to be a powerful tool for many recognition and classification tasks, their stability properties are still not well understood. In the past, image classifiers have been shown to be vulnerable to so-called adversarial attacks, which are created by additively perturbing the correctly... | ADEF: AN ITERATIVE ALGORITHM TO CONSTRUCT ADVERSARIAL DEFORMATIONS |
d13900194 | Neural networks are vulnerable to adversarial examples and researchers have proposed many heuristic attack and defense mechanisms. We address this problem through the principled lens of distributionally robust optimization, which guarantees performance under adversarial input perturbations. By considering a Lagrangian ... | Certifying Some Distributional Robustness with Principled Adversarial Training |
d220666191 | We construct an unsupervised learning model that achieves nonlinear disentanglement of underlying factors of variation in naturalistic videos. Previous work suggests that representations can be disentangled if all but a few factors in the environment stay constant at any point in time. As a result, algorithms proposed ... | Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding |
d258865994 | We introduce Point2SSM, a novel unsupervised learning approach that can accurately construct correspondence-based statistical shape models (SSMs) of anatomy directly from point clouds. SSMs are crucial in clinical research for analyzing the population-level morphological variation in bones and organs. However, traditio... | Point2SSM: Learning Morphological Variations of Anatomies from Point Clouds |
d43968607 | We introduce hyperbolic attention networks to endow neural networks with enough capacity to match the complexity of data with hierarchical and power-law structure. A few recent approaches have successfully demonstrated the benefits of imposing hyperbolic geometry on the parameters of shallow networks. We extend this li... | Hyperbolic Attention Networks |
d252715969 | Contrastive learning is a powerful framework for learning self-supervised representations that generalize well to downstream supervised tasks. We show that multiple existing contrastive learning methods can be reinterpreted as learning kernel functions that approximate a fixed positive-pair kernel. We then prove that a... | CONTRASTIVE LEARNING CAN FIND AN OPTIMAL BASIS FOR APPROXIMATELY VIEW-INVARIANT FUNCTIONS |
d199577730 | The design of deep graph models still remains to be investigated and the crucial part is how to explore and exploit the knowledge from different hops of neighbors in an efficient way. In this paper, we propose a novel RNN-like deep graph neural network architecture by incorporating AdaBoost into the computation of netw... | AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models |
d195766863 | Clustering is an important part of many modern data analysis pipelines, including network analysis and data retrieval. There are many different clustering algorithms developed by various communities, and it is often not clear which algorithm will give the best performance on a specific clustering task. Similarly, we of... | Learning to Link |
d214107001 | Among multiple ways of interpreting a machine learning model, measuring the importance of a set of features tied to a prediction is probably one of the most intuitive ways to explain a model. In this paper, we establish the link between a set of features to a prediction with a new evaluation criterion, robustness analy... | Evaluations and Methods for Explanation through Robustness Analysis |
d258461498 | Dynamic Sparse Training (DST) methods achieve state-of-the-art results in sparse neural network training, matching the generalization of dense models while enabling sparse training and inference.Although the resulting models are highly sparse and theoretically less computationally expensive, achieving speedups with uns... | DYNAMIC SPARSE TRAINING WITH STRUCTURED SPARSITY |
d219573568 | Unsupervised visual pretraining based on the instance discrimination pretext task has shown significant progress. Notably, in the recent work of MoCo, unsupervised pretraining 1 has shown to surpass the supervised counterpart for finetuning downstream applications such as object detection on PASCAL VOC. It comes as a s... | What makes instance discrimination good for transfer learning? |
d257505182 | Text-to-3D generation has shown rapid progress in recent days with the advent of score distillation, a methodology of using pretrained text-to-2D diffusion models to optimize neural radiance field (NeRF) in the zero-shot setting. However, the lack of 3D awareness in the 2D diffusion models destabilizes score distillati... | Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation |
d253097769 | Intelligent agents need to remember salient information to reason in partiallyobserved environments. For example, agents with a first-person view should remember the positions of relevant objects even if they go out of view. Similarly, to effectively navigate through rooms agents need to remember the floor plan of how ... | EVALUATING LONG-TERM MEMORY IN 3D MAZES |
d222310549 | Shape and texture are two prominent and complementary cues for recognizing objects. Nonetheless, Convolutional Neural Networks are often biased towards either texture or shape, depending on the training dataset. Our ablation shows that such bias degenerates model performance. Motivated by this observation, we develop a... | SHAPE-TEXTURE DEBIASED NEURAL NETWORK TRAINING |
d264438904 | Koopman representations aim to learn features of nonlinear dynamical systems (NLDS) which lead to linear dynamics in the latent space.Theoretically, such features can be used to simplify many problems in modeling and control of NLDS.In this work we study autoencoder formulations of this problem, and different ways they... | COURSE CORRECTING KOOPMAN REPRESENTATIONS |
d53113128 | Neural architecture search (NAS) automatically finds the best task-specific neural network topology, outperforming many manual architecture designs. However, it can be prohibitively expensive as the search requires training thousands of different networks, while each can last for hours. In this work, we propose the Gra... | GRAPH HYPERNETWORKS FOR NEURAL ARCHITECTURE SEARCH |
d263830025 | Composed image retrieval (CIR) is the task of retrieving specific images by using a query that involves both a reference image and a relative caption.Most existing CIR models adopt the late-fusion strategy to combine visual and language features.Besides, several approaches have also been suggested to generate a pseudo-... | SENTENCE-LEVEL PROMPTS BENEFIT COMPOSED IMAGE RETRIEVAL |
d221446298 | Poisoning attacks on Reinforcement Learning (RL) systems could take advantage of RL algorithm's vulnerabilities and cause failure of the learning. However, prior works on poisoning RL usually either unrealistically assume the attacker knows the underlying Markov Decision Process (MDP), or directly apply the poisoning m... | VULNERABILITY-AWARE POISONING MECHANISM FOR ONLINE RL WITH UNKNOWN DYNAMICS |
d3516266 | It is common practice to decay the learning rate. Here we show one can usually obtain the same learning curve on both training and test sets by instead increasing the batch size during training. This procedure is successful for stochastic gradient descent (SGD), SGD with momentum, Nesterov momentum, and Adam. It reache... | DON'T DECAY THE LEARNING RATE, INCREASE THE BATCH SIZE |
d263830945 | Graph Neural Networks (GNNs) have shown great promise in learning node embeddings for link prediction (LP).While numerous studies aim to improve the overall LP performance of GNNs, none have explored its varying performance across different nodes and its underlying reasons.To this end, we aim to demystify which nodes w... | A TOPOLOGICAL PERSPECTIVE ON DEMYSTIFYING GNN-BASED LINK PREDICTION PERFORMANCE |
d3278749 | Spectral clustering is a leading and popular technique in unsupervised data analysis. Two of its major limitations are scalability and generalization of the spectral embedding (i.e., out-of-sample-extension). In this paper we introduce a deep learning approach to spectral clustering that overcomes the above shortcoming... | SPECTRALNET: SPECTRAL CLUSTERING USING DEEP NEURAL NETWORKS |
d225062378 | We present a new family of min-max optimization algorithms that automatically exploits the geometry of the gradient data observed at earlier iterations to perform more informative extra-gradient steps in later ones. Thanks to this adaptation mechanism, the proposed methods automatically detect whether the problem is sm... | ADAPTIVE EXTRA-GRADIENT METHODS FOR MIN-MAX OPTIMIZATION AND GAMES |
d260682249 | Large Language Models (LLMs) are becoming increasingly smart and autonomous, targeting real-world pragmatic missions beyond traditional NLP tasks. As a result, there has been an urgent need to evaluate LLMs as agents on challenging tasks in interactive environments. We present AgentBench, a multi-dimensional evolving b... | AgentBench: Evaluating LLMs as Agents |
d231632900 | The mushroom body of the fruit fly brain is one of the best studied systems in neuroscience. At its core it consists of a population of Kenyon cells, which receive inputs from multiple sensory modalities. These cells are inhibited by the anterior paired lateral neuron, thus creating a sparse high dimensional representa... | CAN A FRUIT FLY LEARN WORD EMBEDDINGS? |
d263610128 | By providing external information to large language models (LLMs), tool augmentation (including retrieval augmentation) has emerged as a promising solution for addressing the limitations of LLMs' static parametric memory.However, how receptive are LLMs to such external evidence, especially when the evidence conflicts w... | Adaptive Chameleon or Stubborn Sloth: REVEALING THE BEHAVIOR OF LARGE LANGUAGE MODELS IN KNOWLEDGE CONFLICTS |
d226281747 | We propose a new class of parameterizations for spatio-temporal point processes which leverage Neural ODEs as a computational method and enable flexible, highfidelity models of discrete events that are localized in continuous time and space. Central to our approach is a combination of recurrent continuous-time neural n... | NEURAL SPATIO-TEMPORAL POINT PROCESSES |
d254125609 | Most existing Image Restoration (IR) models are task-specific, which can not be generalized to different degradation operators. In this work, we propose the Denoising Diffusion Null-Space Model (DDNM), a novel zero-shot framework for arbitrary linear IR problems, including but not limited to image super-resolution, col... | ZERO-SHOT IMAGE RESTORATION USING DENOISING DIFFUSION NULL-SPACE MODEL |
d15904815 | Recent years have seen the proposal of a number of neural architectures for the problem of Program Induction. Given a set of input-output examples, these architectures are able to learn mappings that generalize to new test inputs. While achieving impressive results, these approaches have a number of important limitatio... | NEURO-SYMBOLIC PROGRAM SYNTHESIS |
d244713935 | Most graph neural networks (GNNs) use the message passing paradigm, in which node features are propagated on the input graph. Recent works pointed to the distortion of information flowing from distant nodes as a factor limiting the efficiency of message passing for tasks relying on long-distance interactions. This phen... | UNDERSTANDING OVER-SQUASHING AND BOTTLENECKS ON GRAPHS VIA CURVATURE |
d246210185 | A determinantal point process (DPP) on a collection of M items is a model, parameterized by a symmetric kernel matrix, that assigns a probability to every subset of those items. Recent work shows that removing the kernel symmetry constraint, yielding nonsymmetric DPPs (NDPPs), can lead to significant predictive perform... | SCALABLE SAMPLING FOR NONSYMMETRIC DETERMINANTAL POINT PROCESSES |
d252780973 | Forming a molecular candidate set that contains a wide range of potentially effective compounds is crucial to the success of drug discovery. While most databases and machine-learning-based generation models aim to optimize particular chemical properties, there is limited literature on how to properly measure the covera... | HOW MUCH SPACE HAS BEEN EXPLORED? MEASURING THE CHEMICAL SPACE COVERED BY DATABASES AND MACHINE-GENERATED MOLECULES |
d247011642 | In machine learning, we traditionally evaluate the performance of a single model, averaged over a collection of test inputs. In this work, we propose a new approach: we measure the performance of a collection of models when evaluated on a single input point. Speci cally, we study a point's pro le: the relationship betw... | Deconstructing Distributions: A Pointwise Framework of Learning |
d57573766 | Real-world tasks are often highly structured. Hierarchical reinforcement learning (HRL) has attracted research interest as an approach for leveraging the hierarchical structure of a given task in reinforcement learning (RL). However, identifying the hierarchical policy structure that enhances the performance of RL is n... | HIERARCHICAL REINFORCEMENT LEARNING VIA ADVANTAGE-WEIGHTED INFORMATION MAXIMIZATION |
d263609067 | Time-series causal discovery (TSCD) is a fundamental problem of machine learning.However, existing synthetic datasets cannot properly evaluate or predict the algorithms' performance on real data.This study introduces the CausalTime pipeline to generate time-series that highly resemble the real data and with ground trut... | CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery |
d253098130 | While there has been substantial success for solving continuous control with actorcritic methods, simpler critic-only methods such as Q-learning find limited application in the associated high-dimensional action spaces.However, most actorcritic methods come at the cost of added complexity: heuristics for stabilisation,... | SOLVING CONTINUOUS CONTROL VIA Q-LEARNING |
d3489117 | Designing architectures for deep neural networks requires expert knowledge and substantial computation time. We propose a technique to accelerate architecture selection by learning an auxiliary HyperNet that generates the weights of a main model conditioned on that model's architecture. By comparing the relative valida... | SMASH: One-Shot Model Architecture Search through HyperNetworks |
d3522489 | In this work, we face the problem of unsupervised domain adaptation with a novel deep learning approach which leverages on our finding that entropy minimization is induced by the optimal alignment of second order statistics between source and target domains. We formally demonstrate this hypothesis and, aiming at achiev... | MINIMAL-ENTROPY CORRELATION ALIGNMENT FOR UNSUPERVISED DEEP DOMAIN ADAPTATION |
d249191864 | We present a conditional variational auto-encoder (VAE) which, to avoid the substantial cost of training from scratch, uses an architecture and training objective capable of leveraging a foundation model in the form of a pretrained unconditional VAE. To train the conditional VAE, we only need to train an artifact to pe... | CONDITIONAL IMAGE GENERATION BY CONDITIONING VARIATIONAL AUTO-ENCODERS |
d52898806 | Several first order stochastic optimization methods commonly used in the Euclidean domain such as stochastic gradient descent (SGD), accelerated gradient descent or variance reduced methods have already been adapted to certain Riemannian settings. However, some of the most popular of these optimization tools − namely A... | RIEMANNIAN ADAPTIVE OPTIMIZATION METHODS |
d4885767 | We present an efficient coresets-based neural network compression algorithm that provably sparsifies the parameters of a trained fully-connected neural network in a manner that approximately preserves the network's output. Our approach is based on an importance sampling scheme that judiciously defines a sampling distri... | Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds |
d231979429 | A recent line of ground-breaking results for permutation-based SGD has corroborated a widely observed phenomenon: random permutations offer faster convergence than with-replacement sampling. However, is random optimal? We show that this depends heavily on what functions we are optimizing, and the convergence gap betwee... | PERMUTATION-BASED SGD: IS RANDOM OPTIMAL? |
d252907410 | Minimum Description Length (MDL) provides a framework and an objective for principled model evaluation. It formalizes Occam's Razor and can be applied to data from non-stationary sources. In the prequential formulation of MDL, the objective is to minimize the cumulative next-step log-loss when sequentially going throug... | SEQUENTIAL LEARNING OF NEURAL NETWORKS FOR PREQUENTIAL MDL |
d237485378 | In practical situations, the tree ensemble is one of the most popular models along with neural networks. A soft tree is a variant of a decision tree. Instead of using a greedy method for searching splitting rules, the soft tree is trained using a gradient method in which the entire splitting operation is formulated in ... | A Neural Tangent Kernel Perspective of Infinite Tree Ensembles |
d210911499 | Interactive Fiction games are text-based simulations in which an agent interacts with the world purely through natural language. They are ideal environments for studying how to extend reinforcement learning agents to meet the challenges of natural language understanding, partial observability, and action generation in ... | GRAPH CONSTRAINED REINFORCEMENT LEARNING FOR NATURAL LANGUAGE ACTION SPACES |
d246294475 | Large pre-trained language models have been used to generate code, providing a flexible interface for synthesizing programs from natural language specifications. However, they often violate syntactic and semantic rules of their output language, limiting their practical usability. In this paper, we propose SYNCHROMESH: ... | SYNCHROMESH: RELIABLE CODE GENERATION FROM PRE-TRAINED LANGUAGE MODELS |
d49559335 | In standard generative adversarial network (SGAN), the discriminator D estimates the probability that the input data is real. The generator G is trained to increase the probability that fake data is real. We argue that it should also simultaneously decrease the probability that real data is real because 1) this would a... | The relativistic discriminator: a key element missing from standard GAN |
d263608672 | Despite their ubiquity in language generation, it remains unknown why truncation sampling heuristics like nucleus sampling are so effective.We provide a theoretical explanation for the effectiveness of the truncation sampling by proving that truncation methods that discard tokens below some probability threshold (the m... | CLOSING THE CURIOUS CASE OF NEURAL TEXT DEGENERATION |
d52910185 | Addressing uncertainty is critical for autonomous systems to robustly adapt to the real world. We formulate the problem of model uncertainty as a continuous Bayes-Adaptive Markov Decision Process (BAMDP), where an agent maintains a posterior distribution over the latent model parameters given a history of observations ... | Bayesian Policy Optimization for Model Uncertainty |
d252816105 | Shape-based virtual screening is widely employed in ligand-based drug design to search chemical libraries for molecules with similar 3D shapes yet novel 2D chemical structures compared to known ligands. 3D deep generative models have the potential to automate this exploration of shape-conditioned 3D chemical space; how... | EQUIVARIANT SHAPE-CONDITIONED GENERATION OF 3D MOLECULES FOR LIGAND-BASED DRUG DESIGN |
d231925076 | The adaptive stochastic gradient descent (SGD) with momentum has been widely adopted in deep learning as well as convex optimization. In practice, the last iterate is commonly used as the final solution to make decisions. However, the available regret analysis and the setting of constant momentum parameters only guaran... | THE ROLE OF MOMENTUM PARAMETERS IN THE OPTIMAL CONVERGENCE OF ADAPTIVE POLYAK'S HEAVY-BALL METHODS |
d8201526 | Large computer-understandable proofs consist of millions of intermediate logical steps. The vast majority of such steps originate from manually selected and manually guided heuristics applied to intermediate goals. So far, machine learning has generally not been used to filter or generate these steps. In this paper, we... | HOLSTEP: A MACHINE LEARNING DATASET FOR HIGHER-ORDER LOGIC THEOREM PROVING |
d263830071 | In the classical transformer attention scheme, we are given three n × d size matrices Q, K, V (the query, key, and value tokens), and the goal is to compute a new n × d size matrix D −1 exp(QK ⊤ )V where D = diag(exp(QK ⊤ )1 n ). Here, exp() is applied entry-wise and 1 n denotes a length-n vector whose entries are all ... | How to Capture Higher-order Correlations? Generalizing Matrix Softmax Attention to Kronecker Computation |
d249191844 | Measuring the stability of conclusions derived from Ordinary Least Squares linear regression is critically important, but most metrics either only measure local stability (i.e. against infinitesimal changes in the data), or are only interpretable under statistical assumptions. Recent work proposes a simple, global, fin... | Provably Auditing Ordinary Least Squares in Low Dimensions |
d71638 | While great strides have been made in using deep learning algorithms to solve supervised learning tasks, the problem of unsupervised learning -leveraging unlabeled examples to learn about the structure of a domain -remains a difficult unsolved challenge. Here, we explore prediction of future frames in a video sequence ... | Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning |
d235683072 | Model-agnostic meta-learning (MAML) is arguably one of the most popular metalearning algorithms nowadays. Nevertheless, its performance on few-shot classification is far behind many recent algorithms dedicated to the problem. In this paper, we point out several key facets of how to train MAML to excel in few-shot class... | HOW TO TRAIN YOUR MAML TO EXCEL IN FEW-SHOT CLASSIFICATION |
d252668426 | Inverse molecular design is critical in material science and drug discovery, where the generated molecules should satisfy certain desirable properties. In this paper, we propose equivariant energy-guided stochastic differential equations (EEGSDE), a flexible framework for controllable 3D molecule generation under the g... | EQUIVARIANT ENERGY-GUIDED SDE FOR INVERSE MOLECULAR DESIGN |
d264306282 | Prediction sets capture uncertainty by predicting sets of labels rather than individual labels, enabling downstream decisions to conservatively account for all plausible outcomes.Conformal inference algorithms construct prediction sets guaranteed to contain the true label with high probability.These guarantees fail to ... | PAC Prediction Sets Under Label Shift |
d263909148 | Transformers pretrained on diverse tasks exhibit remarkable in-context learning (ICL) capabilities, enabling them to solve unseen tasks solely based on input contexts without adjusting model parameters. In this paper, we study ICL in one of its simplest setups: pretraining a linearly parameterized single-layer linear a... | How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? |
d264147054 | As transformers are equivariant to the permutation of input tokens, encoding the positional information of tokens is necessary for many tasks.However, since existing positional encoding schemes have been initially designed for NLP tasks, their suitability for vision tasks, which typically exhibit different structural p... | GTA: A GEOMETRY-AWARE ATTENTION MECHANISM FOR MULTI-VIEW TRANSFORMERS |
d48352800 | Stochastic gradient Markov chain Monte Carlo (SG-MCMC) has become increasingly popular for simulating posterior samples in large-scale Bayesian modeling. However, existing SG-MCMC schemes are not tailored to any specific probabilistic model, even a simple modification of the underlying dynamical system requires signifi... | Meta-Learning for Stochastic Gradient MCMC |
d252735112 | We evaluate the reasoning abilities of large language models in multilingual settings. We introduce the Multilingual Grade School Math (MGSM) benchmark, by manually translating 250 grade-school math problems from the GSM8K dataset (Cobbe et al., 2021) into ten typologically diverse languages. We find that the ability t... | LANGUAGE MODELS ARE MULTILINGUAL CHAIN-OF-THOUGHT REASONERS |
d204090878 | For bidirectional joint image-text modeling, we develop variational hetero-encoder (VHE) randomized generative adversarial network (GAN), a versatile deep generative model that integrates a probabilistic text decoder, probabilistic image encoder, and GAN into a coherent end-to-end multi-modality learning framework. VHE... | VARIATIONAL HETERO-ENCODER RANDOMIZED GANS FOR JOINT IMAGE-TEXT MODELING |
d246210145 | Computing the matrix square root or its inverse in a differentiable manner is important in a variety of computer vision tasks. Previous methods either adopt the Singular Value Decomposition (SVD) to explicitly factorize the matrix or use the Newton-Schulz iteration (NS iteration) to derive the approximate solution. How... | FAST DIFFERENTIABLE MATRIX SQUARE ROOT |
d252872909 | We consider the estimation of average and counterfactual treatment effects, under two settings: back-door adjustment and frontdoor adjustment. The goal in both cases is to recover the treatment effect without having an access to a hidden confounder. This objective is attained by first estimating the conditional mean of... | A Neural Mean Embedding Approach for Back-door and Front-door Adjustment |
d263620393 | It is inherently ambiguous to lift 2D results from pre-trained diffusion models to a 3D world for text-to-3D generation.2D diffusion models solely learn viewagnostic priors and thus lack 3D knowledge during the lifting, leading to the multiview inconsistency problem.We find that this problem primarily stems from geomet... | SWEETDREAMER: ALIGNING GEOMETRIC PRIORS IN 2D DIFFUSION FOR CONSISTENT TEXT-TO-3D |
d247011196 | We propose an interacting contour stochastic gradient Langevin dynamics (IC-SGLD) sampler, an embarrassingly parallel multiple-chain contour stochastic gradient Langevin dynamics (CSGLD) sampler with efficient interactions. We show that ICSGLD can be theoretically more efficient than a single-chain CSGLD with an equiva... | INTERACTING CONTOUR STOCHASTIC GRADIENT LANGEVIN DYNAMICS |
d233307448 | A popular approach to model compression is to train an inexpensive student model to mimic the class probabilities of a highly accurate but cumbersome teacher model. Surprisingly, this two-step knowledge distillation process often leads to higher accuracy than training the student directly on labeled data. To explain an... | KNOWLEDGE DISTILLATION AS SEMIPARAMETRIC INFERENCE |
d247476286 | Vision transformers (ViTs) have recently set off a new wave in neural architecture design thanks to their record-breaking performance in various vision tasks. In parallel, to fulfill the goal of deploying ViTs into real-world vision applications, their robustness against potential malicious attacks has gained increasin... | PATCH-FOOL: ARE VISION TRANSFORMERS ALWAYS ROBUST AGAINST ADVERSARIAL PERTURBATIONS? |
d246431258 | Visual object tracking (VOT) has been widely adopted in mission-critical applications, such as autonomous driving and intelligent surveillance systems. In current practice, third-party resources such as datasets, backbone networks, and training platforms are frequently used to train high-performance VOT models. Whilst ... | FEW-SHOT BACKDOOR ATTACKS ON VISUAL OBJECT TRACKING |
d221516648 | Recent work has shown that large text-based neural language models, trained with conventional supervised learning objectives, acquire a surprising propensity for few-and one-shot learning. Here, we show that an embodied agent situated in a simulated 3D world, and endowed with a novel dual-coding external memory, can ex... | Grounded Language Learning Fast and Slow |
d2129889 | End-to-end dialog systems, in which all components are learnt simultaneously, have recently obtained encouraging successes. However these were mostly on conversations related to chit-chat with no clear objective and for which evaluation is difficult. This paper proposes a set of tasks to test the capabilities of such s... | Learning End-to-End Goal-Oriented Dialog |
d53464644 | Inferring the structural properties of a protein from its amino acid sequence is a challenging yet important problem in biology. Structures are not known for the vast majority of protein sequences, but structure is critical for understanding function. Existing approaches for detecting structural similarity between prot... | LEARNING PROTEIN SEQUENCE EMBEDDINGS USING INFORMATION FROM STRUCTURE |
d244527640 | Most set prediction models in deep learning use set-equivariant operations, but they actually operate on multisets. We show that set-equivariant functions cannot represent certain functions on multisets, so we introduce the more appropriate notion of multiset-equivariance. We identify that the existing Deep Set Predict... | MULTISET-EQUIVARIANT SET PREDICTION WITH APPROXIMATE IMPLICIT DIFFERENTIATION |
d54443381 | In lifelong learning, the learner is presented with a sequence of tasks, incrementally building a data-driven prior which may be leveraged to speed up learning of a new task. In this work, we investigate the efficiency of current lifelong approaches, in terms of sample complexity, computational and memory cost. Towards... | EFFICIENT LIFELONG LEARNING WITH A-GEM |
d54458552 | The unconditional generation of high fidelity images is a longstanding benchmark for testing the performance of image decoders. Autoregressive image models have been able to generate small images unconditionally, but the extension of these methods to large images where fidelity can be more readily assessed has remained... | GENERATING HIGH FIDELITY IMAGES WITH SUBSCALE PIXEL NETWORKS AND MULTIDIMENSIONAL UPSCALING |
d264128166 | Retrosynthesis is the task of proposing a series of chemical reactions to create a desired molecule from simpler, buyable molecules.While previous works have proposed algorithms to find optimal solutions for a range of metrics (e.g.shortest, lowest-cost), these works generally overlook the fact that we have imperfect k... | RETRO-FALLBACK: RETROSYNTHETIC PLANNING IN AN UNCERTAIN WORLD |
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