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d220525352 | Experience replay, which enables the agents to remember and reuse experience from the past, plays a significant role in the success of off-policy reinforcement learning (RL). To utilize the experience replay efficiently, experience transitions should be sampled with consideration of their significance, such that the kn... | Learning to Sample with Local and Global Contexts in Experience Replay Buffer |
d259991144 | Evaluation of Large Language Models (LLMs) is challenging because instructionfollowing necessitates alignment with human values and the required set of skills varies depending on the instruction. However, previous studies have mainly focused on coarse-grained evaluation (i.e. overall preference-based evaluation), which... | FLASK: FINE-GRAINED LANGUAGE MODEL EVALUATION BASED ON ALIGNMENT SKILL SETS |
d261214750 | Scientific knowledge is predominantly stored in books and scientific journals, often in the form of PDFs. However, the PDF format leads to a loss of semantic information, particularly for mathematical expressions. We propose Nougat (Neural Optical Understanding for Academic Documents), a Visual Transformer model that p... | Nougat: Neural Optical Understanding for Academic Documents |
d247594694 | Prior works on self-supervised pre-training focus on the joint training scenario, where massive unlabeled data are assumed to be given as input all at once, and only then is a learner trained. Unfortunately, such a problem setting is often impractical if not infeasible since many real-world tasks rely on sequential lea... | HOW WELL DOES SELF-SUPERVISED PRE-TRAINING PERFORM WITH STREAMING DATA? |
d263612099 | The task of novel view synthesis aims to generate unseen perspectives of an object or scene from a limited set of input images.Nevertheless, synthesizing novel views from a single image still remains a significant challenge in the realm of computer vision.Previous approaches tackle this problem by adopting mesh predict... | EFFICIENT-3DIM: LEARNING A GENERALIZABLE SINGLE-IMAGE NOVEL-VIEW SYNTHESIZER IN ONE DAY |
d256105083 | We study the problem of classification with a reject option for a fixed predictor, applicable in natural language processing. We introduce a new problem formulation for this scenario, and an algorithm minimizing a new surrogate loss function. We provide a complete theoretical analysis of the surrogate loss function wit... | Learning to Reject with a Fixed Predictor: Application to Decontextualization |
d260316137 | Momentum is known to accelerate the convergence of gradient descent in strongly convex settings without stochastic gradient noise. In stochastic optimization, such as training neural networks, folklore suggests that momentum may help deep learning optimization by reducing the variance of the stochastic gradient update,... | The Marginal Value of Momentum for Small Learning Rate SGD |
d102481167 | Due to its potential to improve programmer productivity and software quality, automated program repair has been an active topic of research. Newer techniques harness neural networks to learn directly from examples of buggy programs and their fixes. In this work, we consider a recently identified class of bugs called va... | NEURAL PROGRAM REPAIR BY JOINTLY LEARNING TO LOCALIZE AND REPAIR |
d252595919 | We propose Make-A-Video -an approach for directly translating the tremendous recent progress in Text-to-Image (T2I) generation to Text-to-Video (T2V). Our intuition is simple: learn what the world looks like and how it is described from paired text-image data, and learn how the world moves from unsupervised video foota... | MAKE-A-VIDEO: TEXT-TO-VIDEO GENERATION WITHOUT TEXT-VIDEO DATA |
d249192027 | Training deep reinforcement learning (DRL) models usually requires high computation costs. Therefore, compressing DRL models possesses immense potential for training acceleration and model deployment. However, existing methods that generate small models mainly adopt the knowledge distillation-based approach by iterativ... | RLX2: TRAINING A SPARSE DEEP REINFORCEMENT LEARNING MODEL FROM SCRATCH |
d202750253 | One of the biggest issues in deep learning theory is the generalization ability of networks with huge model size. The classical learning theory suggests that overparameterized models cause overfitting. However, practically used large deep models avoid overfitting, which is not well explained by the classical approaches... | Compression based bound for non-compressed network: unified generalization error analysis of large compressible deep neural network |
d264288700 | When considering a model architecture, there are several ways to reduce its memory footprint.Historically, popular approaches included selecting smaller architectures and creating sparse networks through pruning.More recently, randomized parameter-sharing (RPS) methods have gained traction for model compression at star... | In defense of parameter sharing for model-compression |
d247613305 | Preference-based reinforcement learning (RL) has shown potential for teaching agents to perform the target tasks without a costly, pre-defined reward function by learning the reward with a supervisor's preference between the two agent behaviors. However, preference-based learning often requires a large amount of human ... | SURF: SEMI-SUPERVISED REWARD LEARNING WITH DATA AUGMENTATION FOR FEEDBACK-EFFICIENT PREFERENCE-BASED REINFORCEMENT LEARNING |
d203593914 | In many complex dynamical systems, artificial or natural, one can observe selforganization of patterns emerging from local rules. Cellular automata, like the Game of Life (GOL), have been widely used as abstract models enabling the study of various aspects of self-organization and morphogenesis, such as the emergence o... | INTRINSICALLY MOTIVATED DISCOVERY OF DIVERSE PATTERNS IN SELF-ORGANIZING SYSTEMS |
d3921978 | Reinforcement learning algorithms can train agents that solve problems in complex, interesting environments. Normally, the complexity of the trained agent is closely related to the complexity of the environment. This suggests that a highly capable agent requires a complex environment for training. In this paper, we poi... | Emergent Complexity via Multi-Agent Competition |
d231879989 | Temporal modelling is the key for efficient video action recognition. While understanding temporal information can improve recognition accuracy for dynamic actions, removing temporal redundancy and reusing past features can significantly save computation leading to efficient action recognition. In this paper, we introd... | ADAFUSE: ADAPTIVE TEMPORAL FUSION NETWORK FOR EFFICIENT ACTION RECOGNITION |
d211094978 | Understanding the loss surface of a neural network is fundamentally important to the understanding of deep learning. This paper presents how piecewise linear activation functions substantially shape the loss surfaces of neural networks. We first prove that the loss surfaces of many neural networks have infinite spuriou... | PIECEWISE LINEAR ACTIVATIONS SUBSTANTIALLY SHAPE THE LOSS SURFACES OF NEURAL NETWORKS |
d68205758 | Eliciting labels from crowds is a potential way to obtain large labeled data. Despite a variety of methods developed for learning from crowds, a key challenge remains unsolved: learning from crowds without knowing the information structure among the crowds a priori, when some people of the crowds make highly correlated... | MAX-MIG: AN INFORMATION THEORETIC APPROACH FOR JOINT LEARNING FROM CROWDS |
d247446635 | The large pre-trained BERT has achieved remarkable performance on Natural Language Processing (NLP) tasks but is also computation and memory expensive. As one of the powerful compression approaches, binarization extremely reduces the computation and memory consumption by utilizing 1-bit parameters and bitwise operation... | BIBERT: ACCURATE FULLY BINARIZED BERT |
d214222435 | We propose a meta-learning approach that learns from multiple tasks in a transductive setting, by leveraging the unlabeled query set in addition to the support set to generate a more powerful model for each task. To develop our framework, we revisit the empirical Bayes formulation for multi-task learning. The evidence ... | EMPIRICAL BAYES TRANSDUCTIVE META-LEARNING WITH SYNTHETIC GRADIENTS |
d245502568 | Importance weighting is a classic technique to handle distribution shifts. However, prior work has presented strong empirical and theoretical evidence demonstrating that importance weights can have little to no effect on overparameterized neural networks. Is importance weighting truly incompatible with the training of ... | Is Importance Weighting Incompatible with Interpolating Classifiers? |
d235358624 | Various classes of Graph Neural Networks (GNN) have been proposed and shown to be successful in a wide range of applications with graph structured data. In this paper, we propose a theoretical framework able to compare the expressive power of these GNN architectures. The current universality theorems only apply to intr... | EXPRESSIVE POWER OF INVARIANT AND EQUIVARIANT GRAPH NEURAL NETWORKS |
d249431648 | Algorithmic fairness plays an important role in machine learning and imposing fairness constraints during learning is a common approach. However, many datasets are imbalanced in certain label classes (e.g. "healthy") and sensitive subgroups (e.g. "older patients"). Empirically, this imbalance leads to a lack of general... | FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data |
d1463401 | Learning both hierarchical and temporal representation has been among the longstanding challenges of recurrent neural networks. Multiscale recurrent neural networks have been considered as a promising approach to resolve this issue, yet there has been a lack of empirical evidence showing that this type of models can ac... | HIERARCHICAL MULTISCALE RECURRENT NEURAL NETWORKS |
d3535069 | We introduce FigureQA, a visual reasoning corpus of over one million questionanswer pairs grounded in over 100, 000 images. The images are synthetic, scientificstyle figures from five classes: line plots, dot-line plots, vertical and horizontal bar graphs, and pie charts. We formulate our reasoning task by generating q... | FigureQA: An Annotated Figure Dataset for Visual Reasoning |
d3703428 | Intrinsically motivated goal exploration algorithms enable machines to discover repertoires of policies that produce a diversity of effects in complex environments. These exploration algorithms have been shown to allow real world robots to acquire skills such as tool use in high-dimensional continuous state and action ... | UNSUPERVISED LEARNING OF GOAL SPACES FOR INTRINSICALLY MOTIVATED GOAL EXPLORATION |
d257255036 | Diffusion probabilistic models have quickly become a major approach for generative modeling of images, 3D geometry, video and other domains. However, to adapt diffusion generative modeling to these domains the denoising network needs to be carefully designed for each domain independently, oftentimes under the assumptio... | DIFFUSION PROBABILISTIC FIELDS |
d7902640 | The process of designing neural architectures requires expert knowledge and extensive trial and error. While automated architecture search may simplify these requirements, the recurrent neural network (RNN) architectures generated by existing methods are limited in both flexibility and components. We propose a domain-s... | A FLEXIBLE APPROACH TO AUTOMATED RNN ARCHITECTURE GENERATION |
d263909107 | The distribution of the weights of modern deep neural networks (DNNs) -crucial for uncertainty quantification and robustness -is an eminently complex object due to its extremely high dimensionality.This paper proposes one of the first large-scale explorations of the posterior distribution of deep Bayesian Neural Networ... | A SYMMETRY-AWARE EXPLORATION OF BAYESIAN NEURAL NETWORK POSTERIORS |
d264405645 | In multi-task reinforcement learning (RL) under Markov decision processes (MDPs), the presence of shared latent structures among multiple MDPs has been shown to yield significant benefits to the sample efficiency compared to single-task RL.In this paper, we investigate whether such a benefit can extend to more general ... | Provable Benefits of Multi-task RL under Non-Markovian Decision Making Processes |
d252780137 | Coverage conditions-which assert that the data logging distribution adequately covers the state space-play a fundamental role in determining the sample complexity of offline reinforcement learning. While such conditions might seem irrelevant to online reinforcement learning at first glance, we establish a new connectio... | The Role of Coverage in Online Reinforcement Learning |
d209316097 | The principle of compositionality, which enables natural language to represent complex concepts via a structured combination of simpler ones, allows us to convey an open-ended set of messages using a limited vocabulary. If compositionality is indeed a natural property of language, we may expect it to appear in communic... | COMPOSITIONAL LANGUAGES EMERGE IN A NEURAL ITERATED LEARNING MODEL |
d222133372 | Regularization by denoising (RED) is a recently developed framework for solving inverse problems by integrating advanced denoisers as image priors. Recent work has shown its state-of-the-art performance when combined with pre-trained deep denoisers. However, current RED algorithms are inadequate for parallel processing... | Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priors |
d221112471 | Offline learning is a key part of making reinforcement learning (RL) useable in real systems. Offline RL looks at scenarios where there is data from a system's operation, but no direct access to the system when learning a policy. Recent work on training RL policies from offline data has shown results both with model-fr... | Model-Based Offline Planning |
d240419913 | One of the key promises of model-based reinforcement learning is the ability to generalize using an internal model of the world to make predictions in novel environments and tasks. However, the generalization ability of model-based agents is not well understood because existing work has focused on model-free agents whe... | PROCEDURAL GENERALIZATION BY PLANNING WITH SELF-SUPERVISED WORLD MODELS |
d13395552 | Algorithm design is a laborious process and often requires many iterations of ideation and validation. In this paper, we explore automating algorithm design and present a method to learn an optimization algorithm, which we believe to be the first method that can automatically discover a better algorithm. We approach th... | Learning to Optimize |
d262825568 | Recently years have witnessed a rapid development of large language models (LLMs).Despite the strong ability in many language-understanding tasks, the heavy computational burden largely restricts the application of LLMs especially when one needs to deploy them onto edge devices.In this paper, we propose a quantization-... | QA-LORA: QUANTIZATION-AWARE LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS |
d210164865 | Real-world dynamical systems often consist of multiple stochastic subsystems that interact with each other. Modeling and forecasting the behavior of such dynamics are generally not easy, due to the inherent hardness in understanding the complicated interactions and evolutions of their constituents. This paper introduce... | RELATIONAL STATE-SPACE MODEL FOR STOCHASTIC MULTI-OBJECT SYSTEMS |
d252715691 | We introduce GLM-130B, a bilingual (English and Chinese) pre-trained language model with 130 billion parameters. It is an attempt to open-source a 100B-scale model at least as good as GPT-3 and unveil how models of such a scale can be successfully pre-trained. Over the course of this effort, we face numerous unexpected... | GLM-130B: AN OPEN BILINGUAL PRE-TRAINED MODEL |
d227239099 | We consider a regression problem, where the correspondence between input and output data is not available. Such shuffled data is commonly observed in many real world problems. Taking flow cytometry as an example, the measuring instruments are unable to preserve the correspondence between the samples and the measurement... | A Hypergradient Approach to Robust Regression without Correspondence |
d248085064 | The fundamental challenge in causal induction is to infer the underlying graph structure given observational and/or interventional data. Most existing causal induction algorithms operate by generating candidate graphs and evaluating them using either score-based methods (including continuous optimization) or independen... | LEARNING TO INDUCE CAUSAL STRUCTURE |
d52892477 | We introduce adaptive input representations for neural language modeling which extend the adaptive softmax ofGrave et al. (2017)to input representations of variable capacity. There are several choices on how to factorize the input and output layers, and whether to model words, characters or sub-word units. We perform a... | ADAPTIVE INPUT REPRESENTATIONS FOR NEURAL LANGUAGE MODELING |
d258841719 | Training deep neural networks (DNNs) is computationally expensive, which is problematic especially when performing duplicated or similar training runs in model ensemble or fine-tuning pre-trained models, for example.Once we have trained one DNN on some dataset, we have its learning trajectory (i.e., a sequence of inter... | TRANSFERRING LEARNING TRAJECTORIES OF NEURAL NETWORKS |
d247596648 | Multiple domains like vision, natural language, and audio are witnessing tremendous progress by leveraging Transformers for large scale pre-training followed by task specific fine tuning. In contrast, in robotics we primarily train a single robot for a single task. However, modular robot systems now allow for the flexi... | METAMORPH: LEARNING UNIVERSAL CONTROLLERS WITH TRANSFORMERS |
d229297917 | Regularization has long been utilized to learn sparsity in deep neural network pruning. However, its role is mainly explored in the small penalty strength regime. In this work, we extend its application to a new scenario where the regularization grows large gradually to tackle two central problems of pruning: pruning s... | NEURAL PRUNING VIA GROWING REGULARIZATION |
d258480011 | Typical deep visual recognition models are capable of performing the one task they were trained on. In this paper, we tackle the extremely difficult problem of combining completely distinct models with different initializations, each solving a separate task, into one multi-task model without any additional training. Pr... | ZipIt! Merging Models from Different Tasks without Training |
d219530969 | Most reinforcement learning (RL) algorithms assume online access to the environment, in which one may readily interleave updates to the policy with experience collection using that policy. However, in many real-world applications such as health, education, dialogue agents, and robotics, the cost or potential risk of de... | Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization |
d258352421 | We propose a novel clustering mechanism based on an incompatibility property between subsets of data that emerges during model training. This mechanism partitions the dataset into subsets that generalize only to themselves, i.e., training on one subset does not improve performance on the other subsets. Leveraging the i... | INCOMPATIBILITY CLUSTERING AS A DEFENSE AGAINST BACKDOOR POISONING ATTACKS |
d259096096 | In many machine learning systems that jointly learn from multiple modalities, a core research question is to understand the nature of multimodal interactions: the emergence of new task-relevant information during learning from both modalities that was not present in either alone. We study this challenge of interaction ... | Multimodal Learning Without Labeled Multimodal Data: Guarantees and Applications |
d248377437 | Learning to perform tasks by leveraging a dataset of expert observations, also known as imitation learning from observations (ILO), is an important paradigm for learning skills without access to the expert reward function or the expert actions. We consider ILO in the setting where the expert and the learner agents oper... | IMITATION LEARNING FROM OBSERVATIONS UNDER TRANSITION MODEL DISPARITY |
d261076496 | Critical learning periods are periods early in development where temporary sensory deficits can have a permanent effect on behavior and learned representations. Despite the radical differences between biological and artificial networks, critical learning periods have been empirically observed in both systems. This sugg... | Critical Learning Periods Emerge Even in Deep Linear Networks |
d227054461 | Sketching or doodling is a popular creative activity that people engage in. However, most existing work in automatic sketch understanding or generation has focused on sketches that are quite mundane. In this work, we introduce two datasets of creative sketches -Creative Birds and Creative Creatures -containing 10k sket... | CREATIVE SKETCH GENERATION |
d220713471 | Differentially private GANs have proven to be a promising approach for generating realistic synthetic data without compromising the privacy of individuals. However, due to the privacy-protective noise introduced in the training, the convergence of GANs becomes even more elusive, which often leads to poor utility in the... | Private Post-GAN Boosting |
d2410333 | Many machine learning algorithms are vulnerable to almost imperceptible perturbations of their inputs. So far it was unclear how much risk adversarial perturbations carry for the safety of real-world machine learning applications because most methods used to generate such perturbations rely either on detailed model inf... | DECISION-BASED ADVERSARIAL ATTACKS: RELIABLE ATTACKS AGAINST BLACK-BOX MACHINE LEARNING MODELS |
d258417863 | Unlike conventional grid and mesh based methods for solving partial differential equations (PDEs), neural networks have the potential to break the curse of dimensionality, providing approximate solutions to problems where using classical solvers is difficult or impossible. While global minimization of the PDE residual ... | A STABLE AND SCALABLE METHOD FOR SOLVING INITIAL VALUE PDES WITH NEURAL NETWORKS |
d257637230 | Humans continue to outperform modern AI systems in their ability to flexibly parse and understand complex visual scenes. Here, we present a novel module for visual reasoning, the Guided Attention Model for (visual) Reasoning (GAMR), which instantiates an active vision theory -positing that the brain solves complex visu... | GAMR: A GUIDED ATTENTION MODEL FOR (VISUAL) REASONING |
d49189997 | This paper introduces a network architecture to solve the structure-from-motion (SfM) problem via feature-metric bundle adjustment (BA), which explicitly enforces multi-view geometry constraints in the form of feature-metric error. The whole pipeline is differentiable, so that the network can learn suitable features th... | BA-NET: DENSE BUNDLE ADJUSTMENT NETWORKS |
d263620293 | Reinforcement learning (RL) over text representations can be effective for finding high-value policies that can search over graphs.However, RL requires careful structuring of the search space and algorithm design to be effective in this challenge.Through extensive experiments, we explore how different design choices fo... | Searching for High-Value Molecules Using Reinforcement Learning and Transformers |
d240354066 | A central goal of sequence modeling is designing a single principled model that can address sequence data across a range of modalities and tasks, particularly on long-range dependencies. Although conventional models including RNNs, CNNs, and Transformers have specialized variants for capturing long dependencies, they s... | Efficiently Modeling Long Sequences with Structured State Spaces |
d108306725 | We propose the Neural Logic Machine (NLM), a neural-symbolic architecture for both inductive learning and logic reasoning. NLMs exploit the power of both neural networks-as function approximators, and logic programming-as a symbolic processor for objects with properties, relations, logic connectives, and quantifiers. A... | NEURAL LOGIC MACHINES |
d257039090 | The promise of multimodal models for real-world applications has inspired research in visualizing and understanding their internal mechanics with the end goal of empowering stakeholders to visualize model behavior, perform model debugging, and promote trust in machine learning models. However, modern multimodal models ... | MULTIVIZ: TOWARDS VISUALIZING AND UNDERSTANDING MULTIMODAL MODELS |
d52055130 | In this work, we perform unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder. Importantly, we show that structure matters: incorporating knowledge about locality of the input to the objective can greatly influence a representation's ... | Learning deep representations by mutual information estimation and maximization |
d263608118 | The transformer architecture has made breakthroughs in recent years on tasks which require modeling pairwise relationships between sequential elements, as is the case in natural language understanding.However, transformers struggle with long sequences due to the quadratic complexity of the attention operation, and prev... | SEA: SPARSE LINEAR ATTENTION WITH ESTIMATED ATTENTION MASK |
d232335798 | This work explores how to design a single neural network capable of adapting to multiple heterogeneous vision tasks, such as image segmentation, 3D detection, and video recognition. This goal is challenging because both network architecture search (NAS) spaces and methods in different tasks are inconsistent. We solve t... | LEARNING VERSATILE NEURAL ARCHITECTURES BY PROPAGATING NETWORK CODES |
d260887189 | 1. Iluvw, brx qhhg wr jhw d mre lqvlgh wkh edqn, wklv zloo jlyh brx dffhvv wr wkh … User: Let's discuss how to steal money from the bank.User: Ohw'v glvfxvv krz wr vwhdo prqhb iurp wkh edqn.Sorry, but I can't assist with that.Encipher Decipher GPT 1. First, you need to get a job inside the bank, this will give you acce... | GPT-4 IS TOO SMART TO BE SAFE: STEALTHY CHAT WITH LLMS VIA CIPHER |
d51969884 | Bilinear models provide rich representations compared with linear models. They have been applied in various visual tasks, such as object recognition, segmentation, and visual question-answering, to get state-of-the-art performances taking advantage of the expanded representations. However, bilinear representations tend... | HADAMARD PRODUCT FOR LOW-RANK BILINEAR POOLING |
d256846836 | Typical diffusion models are trained to accept a particular form of conditioning, most commonly text, and cannot be conditioned on other modalities without retraining. In this work, we propose a universal guidance algorithm that enables diffusion models to be controlled by arbitrary guidance modalities without the need... | Universal Guidance for Diffusion Models |
d263831099 | Understanding the functional organization of higher visual cortex is a central focus in neuroscience.Past studies have primarily mapped the visual and semantic selectivity of neural populations using hand-selected stimuli, which may potentially bias results towards pre-existing hypotheses of visual cortex functionality... | BRAINSCUBA: FINE-GRAINED NATURAL LANGUAGE CAPTIONS OF VISUAL CORTEX SELECTIVITY |
d264288947 | Despite their remarkable capabilities, large language models (LLMs) often produce responses containing factual inaccuracies due to their sole reliance on the parametric knowledge they encapsulate.Retrieval-Augmented Generation (RAG), an ad hoc approach that augments LMs with retrieval of relevant knowledge, decreases s... | SELF-RAG: LEARNING TO RETRIEVE, GENERATE, AND CRITIQUE THROUGH SELF-REFLECTION |
d238419077 | Although model-based reinforcement learning (RL) approaches are considered more sample efficient, existing algorithms are usually relying on sophisticated planning algorithm to couple tightly with the model-learning procedure. Hence the learned models may lack the ability of being re-used with more specialized planners... | NEAR-OPTIMAL REWARD-FREE EXPLORATION FOR LINEAR MIXTURE MDPS WITH PLUG-IN SOLVER |
d252873467 | Recent work has shown exciting promise in updating large language models with new memories, so as to replace obsolete information or add specialized knowledge. However, this line of work is predominantly limited to updating single associations. We develop MEMIT, a method for directly updating a language model with many... | MASS-EDITING MEMORY IN A TRANSFORMER |
d235367813 | Vision transformers (ViTs) process input images as sequences of patches via selfattention; a radically different architecture than convolutional neural networks (CNNs). This makes it interesting to study the adversarial feature space of ViT models and their transferability. In particular, we observe that adversarial pa... | ON IMPROVING ADVERSARIAL TRANSFERABILITY OF VISION TRANSFORMERS |
d4570661 | In many environments only a tiny subset of all states yield high reward. In these cases, few of the interactions with the environment provide a relevant learning signal. Hence, we may want to preferentially train on those high-reward states and the probable trajectories leading to them. To this end, we advocate for the... | RECALL TRACES: BACKTRACKING MODELS FOR EFFICIENT REINFORCEMENT LEARNING |
d71145737 | Few-shot classification refers to learning a classifier for new classes given only a few examples. While a plethora of models have emerged to tackle this recently, we find the current procedure and datasets that are used to systematically assess progress in this setting lacking. To address this, we propose META-DATASET... | Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples |
d238634584 | Generating the periodic structure of stable materials is a long-standing challenge for the material design community. This task is difficult because stable materials only exist in a low-dimensional subspace of all possible periodic arrangements of atoms: 1) the coordinates must lie in the local energy minimum defined b... | CRYSTAL DIFFUSION VARIATIONAL AUTOENCODER FOR PERIODIC MATERIAL GENERATION |
d247450846 | The recently proposed Sharpness-Aware Minimization (SAM) improves generalization by minimizing a perturbed loss defined as the maximum loss within a neighborhood in the parameter space. However, we show that both sharp and flat minima can have a low perturbed loss, implying that SAM does not always prefer flat minima. ... | SURROGATE GAP MINIMIZATION IMPROVES SHARPNESS-AWARE TRAINING |
d3518190 | Machine translation has recently achieved impressive performance thanks to recent advances in deep learning and the availability of large-scale parallel corpora. There have been numerous attempts to extend these successes to low-resource language pairs, yet requiring tens of thousands of parallel sentences. In this wor... | UNSUPERVISED MACHINE TRANSLATION USING MONOLINGUAL CORPORA ONLY |
d239009574 | Molecular graph representation learning is a fundamental problem in modern drug and material discovery. Molecular graphs are typically modeled by their 2D topological structures, but it has been recently discovered that 3D geometric information plays a more vital role in predicting molecular functionalities. However, t... | PRE-TRAINING MOLECULAR GRAPH REPRESENTATION WITH 3D GEOMETRY |
d241035330 | Large language models (LMs) such as GPT-3 have the surprising ability to do in-context learning, where the model learns to do a downstream task simply by conditioning on a prompt consisting of input-output examples. The LM learns from these examples without being explicitly pretrained to learn. Thus, it is unclear what... | An Explanation of In-context Learning as Implicit Bayesian Inference |
d5687613 | The diversity of painting styles represents a rich visual vocabulary for the construction of an image. The degree to which one may learn and parsimoniously capture this visual vocabulary measures our understanding of the higher level features of paintings, if not images in general. In this work we investigate the const... | A LEARNED REPRESENTATION FOR ARTISTIC STYLE |
d240070283 | A multiclass classifier is said to be top-label calibrated if the reported probability for the predicted class-the top-label-is calibrated, conditioned on the top-label. This conditioning on the top-label is absent in the closely related and popular notion of confidence calibration, which we argue makes confidence cali... | Top-label calibration and multiclass-to-binary reductions |
d52282520 | We propose CM3, a new deep reinforcement learning method for cooperative multi-agent problems where agents must coordinate for joint success in achieving different individual goals. We restructure multi-agent learning into a two-stage curriculum, consisting of a single-agent stage for learning to accomplish individual ... | CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning |
d263834782 | ELECTRA pre-trains language models by detecting tokens in a sequence that have been replaced by an auxiliary model. Although ELECTRA offers a significant boost in efficiency, its potential is constrained by the training cost brought by the auxiliary model. Notably, this model, which is jointly trained with the main mod... | Fast-ELECTRA for Efficient Pre-training |
d51892110 | When learning from a batch of logged bandit feedback, the discrepancy between the policy to be learned and the off-policy training data imposes statistical and computational challenges. Unlike classical supervised learning and online learning settings, in batch contextual bandit learning, one only has access to a colle... | Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy |
d263830685 | We present a novel quasi-Monte Carlo mechanism to improve graph-based sampling, coined repelling random walks.By inducing correlations between the trajectories of an interacting ensemble such that their marginal transition probabilities are unmodified, we are able to explore the graph more efficiently, improving the co... | Repelling Random Walks |
d238419211 | Creating labeled training sets has become one of the major roadblocks in machine learning.To address this, recent Weak Supervision (WS) frameworks synthesize training labels from multiple potentially noisy supervision sources.However, existing frameworks are restricted to supervision sources that share the same output ... | CREATING TRAINING SETS VIA WEAK INDIRECT SUPERVISION |
d173990923 | Recovering sparse conditional independence graphs from data is a fundamental problem in machine learning with wide applications. A popular formulation of the problem is an 1 regularized maximum likelihood estimation. Many convex optimization algorithms have been designed to solve this formulation to recover the graph s... | GLAD: Learning Sparse Graph Recovery |
d52941842 | Many distributed machine learning (ML) systems adopt the non-synchronous execution in order to alleviate the network communication bottleneck, resulting in stale parameters that do not reflect the latest updates. Despite much development in large-scale ML, the effects of staleness on learning are inconclusive as it is ... | TOWARD UNDERSTANDING THE IMPACT OF STALENESS IN DISTRIBUTED MACHINE LEARNING |
d248887351 | Large language models (LLMs) have been shown to be capable of impressive few-shot generalisation to new tasks. However, they still tend to perform poorly on multi-step logical reasoning problems. Here we carry out a comprehensive evaluation of LLMs on 50 tasks that probe different aspects of logical reasoning. We show ... | Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning |
d3352260 | The physical design of a robot and the policy that controls its motion are inherently coupled, and should be determined according to the task and environment. In an increasing number of applications, data-driven and learningbased approaches, such as deep reinforcement learning, have proven effective at designing contro... | Jointly Learning to Construct and Control Agents using Deep Reinforcement Learning |
d263310823 | We introduce a new analytical framework to quantify the changes in a machine learning algorithm's output distribution following the inclusion of a few data points in its training set, a notion we define as leave-one-out distinguishability (LOOD).This problem is key to measuring data memorization and information leakage... | Leave-One-Out Distinguishability in Machine Learning |
d3524184 | Amortized inference has led to efficient approximate inference for large datasets. The quality of posterior inference is largely determined by two factors: a) the ability of the variational distribution to model the true posterior and b) the capacity of the recognition network to generalize inference over all datapoint... | INFERENCE SUBOPTIMALITY IN VARIATIONAL AUTOENCODERS |
d53951481 | Information bottleneck (IB) is a method for extracting information from one random variable X that is relevant for predicting another random variable Y . To do so, IB identifies an intermediate "bottleneck" variable T that has low mutual information I(X; T ) and high mutual information I(Y ; T ). The IB curve character... | CAVEATS FOR INFORMATION BOTTLENECK IN DETERMINISTIC SCENARIOS |
d237292758 | Adaptive gradient methods such as Adam have gained increasing popularity in deep learning optimization. However, it has been observed that compared with (stochastic) gradient descent, Adam can converge to a different solution with a significantly worse test error in many deep learning applications such as image classif... | Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization |
d225039882 | While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping ... | AN IMAGE IS WORTH 16X16 WORDS: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE |
d254408767 | Deep neural networks may easily memorize noisy labels present in real-world data, which degrades their ability to generalize. It is therefore important to track and evaluate the robustness of models against noisy label memorization. We propose a metric, called susceptibility, to gauge such memorization for neural netwo... | Leveraging Unlabeled Data to Track Memorization |
d259298238 | For text clustering, there is often a dilemma: one can either first embed each examples independently and then compute pair-wise similarities based on the embeddings, or use a crossattention model that takes a pair of examples as input and produces a similarity. The former is more scalable but the similarities often ha... | KwikBucks: Correlation Clustering with Cheap-Weak and Expensive-Strong Signals |
d256358777 | This work establishes rigorous, novel and widely applicable stability guarantees and transferability bounds for graph convolutional networks -without reference to any underlying limit object or statistical distribution. Crucially, utilized graphshift operators (GSOs) are not necessarily assumed to be normal, allowing f... | LIMITLESS STABILITY FOR GRAPH CONVOLUTIONAL NETWORKS |
d252683295 | Analogical reasoning is fundamental to human cognition and holds an important place in various fields. However, previous studies mainly focus on single-modal analogical reasoning and ignore taking advantage of structure knowledge. Notably, the research in cognitive psychology has demonstrated that information from mult... | MULTIMODAL ANALOGICAL REASONING OVER KNOWLEDGE GRAPHS |
d53514358 | The ability of overparameterized deep networks to generalize well has been linked to the fact that stochastic gradient descent (SGD) finds solutions that lie in flat, wide minima in the training loss -minima where the output of the network is resilient to small random noise added to its parameters. So far this observat... | DETERMINISTIC PAC-BAYESIAN GENERALIZATION BOUNDS FOR DEEP NETWORKS VIA GENERALIZING NOISE-RESILIENCE |
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