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d263609202 | We introduce a principled way of computing the Wasserstein distance between two distributions in a federated manner.Namely, we show how to estimate the Wasserstein distance between two samples stored and kept on different devices/clients whilst a central entity/server orchestrates the computations (again, without havin... | FEDERATED WASSERSTEIN DISTANCE |
d7772900 | Deep reinforcement learning algorithms that estimate state and state-action value functions have been shown to be effective in a variety of challenging domains, including learning control strategies from raw image pixels. However, algorithms that estimate state and state-action value functions typically assume a fully ... | Regret Minimization for Partially Observable Deep Reinforcement Learning |
d108304275 | We identify two issues with the family of algorithms based on the Adversarial Imitation Learning framework. The first problem is implicit bias present in the reward functions used in these algorithms. While these biases might work well for some environments, they can also lead to sub-optimal behavior in others. Secondl... | DISCRIMINATOR-ACTOR-CRITIC: ADDRESSING SAMPLE INEFFICIENCY AND REWARD BIAS IN ADVERSARIAL IMITATION LEARNING |
d257279756 | Pseudo-labeling has proven to be a promising semi-supervised learning (SSL) paradigm. Existing pseudo-labeling methods commonly assume that the class distributions of training data are balanced. However, such an assumption is far from realistic scenarios and thus severely limits the performance of current pseudolabelin... | IMBALANCED SEMI-SUPERVISED LEARNING WITH BIAS ADAPTIVE CLASSIFIER |
d222134068 | Masking tokens uniformly at random constitutes a common flaw in the pretraining of Masked Language Models (MLMs) such as BERT. We show that such uniform masking allows an MLM to minimize its training objective by latching onto shallow local signals, leading to pretraining inefficiency and suboptimal downstream performa... | PMI-MASKING: PRINCIPLED MASKING OF CORRELATED SPANS |
d57825721 | Alternatives to recurrent neural networks, in particular, architectures based on attention or convolutions, have been gaining momentum for processing input sequences. In spite of their relevance, the computational properties of these alternatives have not yet been fully explored. We study the computational power of two... | ON THE TURING COMPLETENESS OF MODERN NEURAL NETWORK ARCHITECTURES |
d1541760 | We propose a novel method that makes use of deep neural networks and gradient decent to perform automated design on complex real world engineering tasks. Our approach works by training a neural network to mimic the fitness function of a design optimization task and then, using the differential nature of the neural netw... | AUTOMATED DESIGN USING NEURAL NETWORKS AND GRADIENT DESCENT |
d52891543 | Recent studies have highlighted adversarial examples as a ubiquitous threat to different neural network models and many downstream applications. Nonetheless, as unique data properties have inspired distinct and powerful learning principles, this paper aims to explore their potentials towards mitigating adversarial inpu... | CHARACTERIZING AUDIO ADVERSARIAL EXAMPLES USING TEMPORAL DEPENDENCY |
d174802415 | We propose a novel score-based approach to learning a directed acyclic graph (DAG) from observational data. We adapt a recently proposed continuous constrained optimization formulation to allow for nonlinear relationships between variables using neural networks. This extension allows to model complex interactions while... | GRADIENT-BASED NEURAL DAG LEARNING |
d264128047 | When applied to question answering and other text generation tasks, language models (LMs) may be queried generatively (by sampling answers from their output distribution) or discriminatively (by using them to score or rank a set of candidate outputs).These procedures sometimes yield very different predictions.How do we... | THE CONSENSUS GAME: LANGUAGE MODEL GENERATION VIA EQUILIBRIUM SEARCH |
d264146611 | While large language models based on the transformer architecture have demonstrated remarkable in-context learning (ICL) capabilities, understandings of such capabilities are still in an early stage, where existing theory and mechanistic understanding focus mostly on simple scenarios such as learning simple function cl... | How Do Transformers Learn In-Context Beyond Simple Functions? A Case Study on Learning with Representations |
d58554701 | We present a new theoretical perspective of data noising in recurrent neural network language models(Xie et al., 2017). We show that each variant of data noising is an instance of Bayesian recurrent neural networks with a particular variational distribution (i.e., a mixture of Gaussians whose weights depend on statisti... | VARIATIONAL SMOOTHING IN RECURRENT NEURAL NETWORK LANGUAGE MODELS |
d235417126 | Graph neural networks (GNNs) have shown great prowess in learning representations suitable for numerous graph-based machine learning tasks. When applied to semi-supervised node classification, GNNs are widely believed to work well due to the homophily assumption ("like attracts like"), and fail to generalize to heterop... | IS HOMOPHILY A NECESSITY FOR GRAPH NEURAL NETWORKS? |
d257255341 | Simultaneous machine translation (SiMT) outputs the target sequence while receiving the source sequence, and hence learning when to start translating each target token is the core challenge for SiMT task. However, it is non-trivial to learn the optimal moment among many possible moments of starting translating, as the ... | HIDDEN MARKOV TRANSFORMER FOR SIMULTANEOUS MACHINE TRANSLATION |
d220266076 | Symbolic techniques based on Satisfiability Modulo Theory (SMT) solvers have been proposed for analyzing and verifying neural network properties, but their usage has been fairly limited owing to their poor scalability with larger networks. In this work, we propose a technique for combining gradient-based methods with s... | Scaling Symbolic Methods using Gradients for Neural Model Explanation |
d236154781 | This paper presents a simple MLP-like architecture, CycleMLP, which is a versatile backbone for visual recognition and dense predictions. As compared to modern MLP architectures, e.g. , MLP-Mixer (Tolstikhin et al., 2021), ResMLP(Touvron et al., 2021a), and gMLP(Liu et al., 2021a), whose architectures are correlated to... | CYCLEMLP: A MLP-LIKE ARCHITECTURE FOR DENSE PREDICTION |
d49549950 | Deep neural networks are susceptible to adversarial attacks. In computer vision, well-crafted perturbations to images can cause neural networks to make mistakes such as confusing a cat with a computer. Previous adversarial attacks have been designed to degrade performance of models or cause machine learning models to p... | ADVERSARIAL REPROGRAMMING OF NEURAL NETWORKS |
d263671826 | Current vision-language generative models rely on expansive corpora of paired image-text data to attain optimal performance and generalization capabilities.However, automatically collecting such data (e.g. via large-scale web scraping) leads to low quality and poor image-text correlation, while human annotation is more... | LEVERAGING UNPAIRED DATA FOR VISION-LANGUAGE GENERATIVE MODELS VIA CYCLE CONSISTENCY |
d631460 | This work details CipherGAN, an architecture inspired by CycleGAN used for inferring the underlying cipher mapping given banks of unpaired ciphertext and plaintext. We demonstrate that CipherGAN is capable of cracking language data enciphered using shift and Vigenère ciphers to a high degree of fidelity and for vocabul... | UNSUPERVISED CIPHER CRACKING USING DISCRETE GANS |
d203593945 | Previous work showed empirically that large neural networks can be significantly reduced in size while preserving their accuracy. Model compression became a central research topic, as it is crucial for deployment of neural networks on devices with limited computational and memory resources. The majority of the compress... | DATA-INDEPENDENT NEURAL PRUNING VIA CORESETS |
d196470924 | Imitation learning, followed by reinforcement learning algorithms, is a promising paradigm to solve complex control tasks sample-efficiently. However, learning from demonstrations often suffers from the covariate shift problem, which results in cascading errors of the learned policy. We introduce a notion of conservati... | Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling |
d15494101 | In this work we study the problem of network morphism, an effective learning scheme to morph a well-trained neural network to a new one with the network function completely preserved. Different from existing work where basic morphing types on the layer level were addressed, we target at the central problem of network m... | MODULARIZED MORPHING OF NEURAL NETWORKS |
d252532222 | Neural volumetric representations have shown the potential that Multi-layer Perceptrons (MLPs) can be optimized with multi-view calibrated images to represent scene geometry and appearance without explicit 3D supervision. Object segmentation can enrich many downstream applications based on the learned radiance field. H... | NERF-SOS: ANY-VIEW SELF-SUPERVISED OBJECT SEGMENTATION ON COMPLEX SCENES |
d258888026 | Dropout is a widely utilized regularization technique in the training of neural networks, nevertheless, its underlying mechanism and its impact on achieving good generalization abilities remain poorly understood. In this work, we derive the stochastic modified equations for analyzing the dynamics of dropout, where its ... | Stochastic Modified Equations and Dynamics of Dropout Algorithm |
d68220930 | We propose Regularized Learning under Label shifts (RLLS), a principled and a practical domain-adaptation algorithm to correct for shifts in the label distribution between a source and a target domain. We first estimate importance weights using labeled source data and unlabeled target data, and then train a classifier ... | REGULARIZED LEARNING FOR DOMAIN ADAPTATION UNDER LABEL SHIFTS |
d258417877 | Well-tuned hyperparameters are crucial for obtaining good generalization behavior in neural networks. They can enforce appropriate inductive biases, regularize the model and improve performance -especially in the presence of limited data. In this work, we propose a simple and efficient way for optimizing hyperparameter... | HYPERPARAMETER OPTIMIZATION THROUGH NEURAL NETWORK PARTITIONING |
d244954755 | We develop a new continual meta-learning method to address challenges in sequential multi-task learning. In this setting, the agent's goal is to achieve high reward over any sequence of tasks quickly. Prior meta-reinforcement learning algorithms have demonstrated promising results in accelerating the acquisition of new... | CoMPS: Continual Meta Policy Search |
d219792420 | We study how representation learning can accelerate reinforcement learning from rich observations, such as images, without relying either on domain knowledge or pixel-reconstruction. Our goal is to learn representations that both provide for effective downstream control and invariance to task-irrelevant details. Bisimu... | Learning Invariant Representations for Reinforcement Learning without Reconstruction |
d232275342 | Image and video synthesis are closely related areas aiming at generating content from noise. While rapid progress has been demonstrated in improving imagebased models to handle large resolutions, high-quality renderings, and wide variations in image content, achieving comparable video generation results remains problem... | A GOOD IMAGE GENERATOR IS WHAT YOU NEED FOR HIGH-RESOLUTION VIDEO SYNTHESIS |
d247656619 | Humans commonly solve complex problems by decomposing them into easier subproblems and then combining the subproblem solutions. This type of compositional reasoning permits reuse of the subproblem solutions when tackling future tasks that share part of the underlying compositional structure. In a continual or lifelong ... | MODULAR LIFELONG REINFORCEMENT LEARNING VIA NEURAL COMPOSITION |
d259145334 | Supervised contrastive loss (SCL) is a competitive and often superior alternative to the cross-entropy loss for classification.While prior studies have demonstrated that both losses yield symmetric training representations under balanced data, this symmetry breaks under class imbalances.This paper presents an intriguin... | Symmetric Neural-Collapse Representations with Supervised Contrastive Loss: The Impact of ReLU and Batching |
d259936996 | Text generation under constraints have seen increasing interests in natural language processing, especially with the rapidly improving capabilities of large language models. However, existing benchmarks for constrained generation usually focus on fixed constraint types (e.g. generate a sentence containing certain words... | COLLIE: Systematic Construction of Constrained Text Generation Tasks |
d1844940 | Empirically, neural networks that attempt to learn programs from data have exhibited poor generalizability. Moreover, it has traditionally been difficult to reason about the behavior of these models beyond a certain level of input complexity. In order to address these issues, we propose augmenting neural architectures ... | MAKING NEURAL PROGRAMMING ARCHITECTURES GENERALIZE VIA RECURSION |
d252762561 | The Generative Flow Network (Bengio et al., 2021b, GFlowNet) is a probabilistic framework where an agent learns a stochastic policy for object generation, such that the probability of generating an object is proportional to a given reward function. Its effectiveness has been shown in discovering high-quality and diver... | Generative Augmented Flow Networks |
d257632050 | Many empirical studies have demonstrated the performance benefits of conditional computation in neural networks, including reduced inference time and power consumption. We study the fundamental limits of neural conditional computation from the perspective of memorization capacity. For Rectified Linear Unit (ReLU) netwo... | Memorization Capacity of Neural Networks with Conditional Computation |
d222208650 | Inverse Reinforcement Learning (IRL) aims to facilitate a learner's ability to imitate expert behavior by acquiring reward functions that explain the expert's decisions. Regularized IRL applies convex regularizers to the learner's policy in order to avoid the expert's behavior being rationalized by arbitrary constant r... | REGULARIZED INVERSE REINFORCEMENT LEARNING |
d259138821 | Modular approaches, which use a different composition of modules for each problem and avoid forgetting by design, have been shown to be a promising direction in continual learning (CL). However, searching through the large, discrete space of possible module compositions is a challenge because evaluating a composition's... | A Probabilistic Framework for Modular Continual Learning |
d259076022 | We propose PolyVoice, a language modelbased framework for speech-to-speech translation (S2ST) system. Our framework consists of two language models: a translation language model and a speech synthesis language model. We use discretized speech units, which are generated in a fully unsupervised way, and thus our framewor... | PolyVoice: Language Models for Speech to Speech Translation |
d8227328 | Our formal understanding of the inductive bias that drives the success of convolutional networks on computer vision tasks is limited. In particular, it is unclear what makes hypotheses spaces born from convolution and pooling operations so suitable for natural images. In this paper we study the ability of convolutional... | Inductive Bias of Deep Convolutional Networks through Pooling Geometry |
d263908842 | In this work, we present an approach to construct a video-based robot policy capable of reliably executing diverse tasks across different robots and environments from few video demonstrations without using any action annotations. Our method leverages images as a task-agnostic representation, encoding both the state and... | LEARNING TO ACT FROM ACTIONLESS VIDEOS THROUGH DENSE CORRESPONDENCES |
d259096014 | Since real-world machine systems are running in non-stationary environments, Continual Test-Time Adaptation (CTTA) task is proposed to adapt the pre-trained model to continually changing target domains. Recently, existing methods mainly focus on model-based adaptation, which aims to leverage a self-training manner to e... | VIDA: HOMEOSTATIC VISUAL DOMAIN ADAPTER FOR CONTINUAL TEST TIME ADAPTATION |
d214390104 | Most algorithms for representation learning and link prediction in relational data have been designed for static data. However, the data they are applied to usually evolves with time, such as friend graphs in social networks or user interactions with items in recommender systems. This is also the case for knowledge bas... | TENSOR DECOMPOSITIONS FOR TEMPORAL KNOWLEDGE BASE COMPLETION |
d257427126 | Outlier exposure (OE) is powerful in out-of-distribution (OOD) detection, enhancing detection capability via model fine-tuning with surrogate OOD data. However, surrogate data typically deviate from test OOD data. Thus, the performance of OE, when facing unseen OOD data, can be weakened. To address this issue, we propo... | OUT-OF-DISTRIBUTION DETECTION WITH IMPLICIT OUTLIER TRANSFORMATION |
d263829192 | Large language models are typically aligned with human preferences by optimizing reward models (RMs) fitted to human feedback.However, human preferences are multi-faceted, and it is increasingly common to derive reward from a composition of simpler reward models which each capture a different aspect of language quality... | CONFRONTING REWARD MODEL OVEROPTIMIZATION WITH CONSTRAINED RLHF |
d247939701 | Model-based reinforcement learning (MBRL) is a sample efficient technique to obtain control policies, yet unavoidable modeling errors often lead to performance deterioration. The model in MBRL is often solely fitted to reconstruct dynamics, state observations in particular, while the impact of model error on the policy... | VALUE GRADIENT WEIGHTED MODEL-BASED REINFORCEMENT LEARNING |
d258999763 | 3 Google DeepMind 4 Weizmann Institute https://hila-chefer.github.io/Conceptor/Concept: painter Concept: sweet peppers fingers pepper CONCEPTOR CONCEPTOR Concept: beetle hornet emerald CONCEPTOR Picasso Monet Impressionism brushes paint portrait + + (a) Concept decomposition with CONCEPTOR (b) Single-image decompositio... | The Hidden Language of Diffusion Models |
d234487049 | Source code summarization aims to generate natural language summaries from structured code snippets for better understanding code functionalities. However, automatic code summarization is challenging due to the complexity of the source code and the language gap between the source code and natural language summaries. Mo... | RETRIEVAL-AUGMENTED GENERATION FOR CODE SUMMARIZATION VIA HYBRID GNN |
d239049480 | Intrinsic rewards play a central role in handling the exploration-exploitation trade-off when designing sequential decision-making algorithms, in both foundational theory and state-of-the-art deep reinforcement learning. The LinUCB algorithm, a centerpiece of the stochastic linear bandits literature, prescribes an elli... | ANTI-CONCENTRATED CONFIDENCE BONUSES FOR SCALABLE EXPLORATION |
d204788559 | Finding an embedding space for a linear approximation of a nonlinear dynamical system enables efficient system identification and control synthesis.The Koopman operator theory lays the foundation for identifying the nonlinear-to-linear coordinate transformations with data-driven methods.Recently, researchers have propo... | LEARNING COMPOSITIONAL KOOPMAN OPERATORS FOR MODEL-BASED CONTROL |
d246240998 | In model extraction attacks, adversaries can steal a machine learning model exposed via a public API by repeatedly querying it and adjusting their own model based on obtained predictions. To prevent model stealing, existing defenses focus on detecting malicious queries, truncating, or distorting outputs, thus necessari... | INCREASING THE COST OF MODEL EXTRACTION WITH CALIBRATED PROOF OF WORK |
d233296562 | It is widely believed that natural image data exhibits low-dimensional structure despite the high dimensionality of conventional pixel representations. This idea underlies a common intuition for the remarkable success of deep learning in computer vision. In this work, we apply dimension estimation tools to popular data... | THE INTRINSIC DIMENSION OF IMAGES AND ITS IMPACT ON LEARNING |
d245005710 | Machine learning systems deployed in the wild are often trained on a source distribution but deployed on a different target distribution. Unlabeled data can be a powerful point of leverage for mitigating these distribution shifts, as it is frequently much more available than labeled data and can often be obtained from ... | Extending the WILDS Benchmark for Unsupervised Adaptation |
d203593433 | Modern generative learning is mainly associated with Generative Adversarial Networks (GANs). Training such networks is always hard due to the minimax nature of the optimization objective. In this paper we propose a novel algorithm for training generative models, which gets rid of minimax GAN objective, thus significant... | Wasserstein-2 Generative Networks |
d173990671 | Event-based neuromorphic systems promise to reduce the energy consumption of deep learning tasks by replacing expensive floating point operations on dense matrices by low power sparse and asynchronous operations on spike events. While these systems can be trained increasingly well using approximations of the backpropag... | SpikeGrad: An ANN-equivalent Computation Model for Implementing Backpropagation with Spikes |
d258107969 | Concept bottleneck models (CBM) are a popular way of creating more interpretable neural networks by having hidden layer neurons correspond to humanunderstandable concepts. However, existing CBMs and their variants have two crucial limitations: first, they need to collect labeled data for each of the predefined concepts... | LABEL-FREE CONCEPT BOTTLENECK MODELS |
d251719133 | Planning-based reinforcement learning has shown strong performance in tasks in discrete and low-dimensional continuous action spaces. However, planning usually brings significant computational overhead for decision-making, and scaling such methods to high-dimensional action spaces remains challenging. To advance effici... | EFFICIENT PLANNING IN A COMPACT LATENT ACTION SPACE |
d246634432 | The numerical solution of partial differential equations (PDEs) is difficult, having led to a century of research so far. Recently, there have been pushes to build neural-numerical hybrid solvers, which piggy-backs the modern trend towards fully end-to-end learned systems. Most works so far can only generalize over a s... | MESSAGE PASSING NEURAL PDE SOLVERS |
d235828929 | Generative Adversarial Networks (GANs) are commonly used for modeling complex distributions of data. Both the generators and discriminators of GANs are often modeled by neural networks, posing a non-transparent optimization problem which is non-convex and non-concave over the generator and discriminator, respectively. ... | HIDDEN CONVEXITY OF WASSERSTEIN GANS: INTERPRETABLE GENERATIVE MODELS WITH CLOSED-FORM SOLUTIONS |
d261682321 | Recently, the remarkable advance of the Large Language Model (LLM) has inspired researchers to transfer its extraordinary reasoning capability to both vision and language data. However, the prevailing approaches primarily regard the visual input as a prompt and focus exclusively on optimizing the text generation proces... | UNIFIED LANGUAGE-VISION PRETRAINING IN LLM WITH DYNAMIC DISCRETE VISUAL TOKENIZATION |
d209531816 | We analyze the Gambler's problem, a simple reinforcement learning problem where the gambler has the chance to double or lose their bets until the target is reached. This is an early example introduced in the reinforcement learning textbook bySutton & Barto (2018), where they mention an interesting pattern of the optima... | THE GAMBLER'S PROBLEM AND BEYOND |
d264555578 | We show that large language models (LLMs) can be adapted to be generalizable policies for embodied visual tasks.Our approach, called Large LAnguage model Reinforcement Learning Policy (LLaRP), adapts a pre-trained frozen LLM to take as input text instructions and visual egocentric observations and output actions direct... | LARGE LANGUAGE MODELS AS GENERALIZABLE POLICIES FOR EMBODIED TASKS |
d246996576 | Graph neural networks (GNNs) have drawn significant research attention recently, mostly under the setting of semi-supervised learning. When task-agnostic representations are preferred or supervision is simply unavailable, the auto-encoder framework comes in handy with a natural graph reconstruction objective for unsupe... | GRAPH AUTO-ENCODER VIA NEIGHBORHOOD WASSERSTEIN RECONSTRUCTION |
d235613604 | Generating future frames given a few context (or past) frames is a challenging task.It requires modeling the temporal coherence of videos and multi-modality in terms of diversity in the potential future states.Current variational approaches for video generation tend to marginalize over multi-modal future outcomes.Inste... | DIVERSE VIDEO GENERATION USING A GAUSSIAN PROCESS TRIGGER |
d252693405 | We tackle the problem of aligning pre-trained large language models (LMs) with human preferences. If we view text generation as a sequential decision-making problem, reinforcement learning (RL) appears to be a natural conceptual framework. However, using RL for LM-based generation faces empirical challenges, including ... | IS REINFORCEMENT LEARNING (NOT) FOR NATURAL LANGUAGE PROCESSING: BENCHMARKS, BASELINES, AND BUILDING BLOCKS FOR NATURAL LANGUAGE POLICY OPTIMIZATION |
d3527879 | This paper introduces a novel method to perform transfer learning across domains and tasks, formulating it as a problem of learning to cluster. The key insight is that, in addition to features, we can transfer similarity information and this is sufficient to learn a similarity function and clustering network to perform... | LEARNING TO CLUSTER IN ORDER TO TRANSFER ACROSS DOMAINS AND TASKS |
d251564597 | In this paper, we study the problem of 3D scene geometry decomposition and manipulation from 2D views. By leveraging the recent implicit neural representation techniques, particularly the appealing neural radiance fields, we introduce an object field component to learn unique codes for all individual objects in 3D spac... | DM-NERF: 3D SCENE GEOMETRY DECOMPOSITION AND MANIPULATION FROM 2D IMAGES |
d24782789 | We discuss the feasibility of the following learning problem: given unmatched samples from two domains and nothing else, learn a mapping between the two, which preserves semantics. Due to the lack of paired samples and without any definition of the semantic information, the problem might seem ill-posed. Specifically, i... | THE ROLE OF MINIMAL COMPLEXITY FUNCTIONS IN UNSUPERVISED LEARNING OF SEMANTIC MAPPINGS |
d235490410 | Popular approaches for minimizing loss in data-driven learning often involve an abstraction or an explicit retention of the history of gradients for efficient parameter updates. The aggregated history of gradients nudges the parameter updates in the right direction even when the gradients at any given step are not info... | Memory Augmented Optimizers for Deep Learning |
d211296302 | We propose a new framework for reasoning about information in complex systems. Our foundation is based on a variational extension of Shannon's information theory that takes into account the modeling power and computational constraints of the observer. The resulting predictive V-information encompasses mutual informatio... | A THEORY OF USABLE INFORMATION UNDER COMPUTATIONAL CONSTRAINTS |
d28202810 | Model-free deep reinforcement learning (RL) algorithms have been demonstrated on a range of challenging decision making and control tasks. However, these methods typically suffer from two major challenges: very high sample complexity and brittle convergence properties, which necessitate meticulous hyperparameter tuning... | Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor |
d53408116 | We introduce a parameter sharing scheme, in which different layers of a convolutional neural network (CNN) are defined by a learned linear combination of parameter tensors from a global bank of templates. Restricting the number of templates yields a flexible hybridization of traditional CNNs and recurrent networks. Com... | LEARNING IMPLICITLY RECURRENT CNNS THROUGH PARAMETER SHARING |
d237563246 | Similarity-based clustering methods separate data into clusters according to the pairwise similarity between the data, and the pairwise similarity is crucial for their performance. In this paper, we propose Clustering by Discriminative Similarity (CDS), a novel method which learns discriminative similarity for data clu... | DISCRIMINATIVE SIMILARITY FOR DATA CLUSTERING |
d225094501 | From the perspective of expressive power, this work compares multi-layer Graph Neural Networks (GNNs) with a simplified alternative that we call Graph-Augmented Multi-Layer Perceptrons (GA-MLPs), which first augments node features with certain multi-hop operators on the graph and then applies an MLP in a node-wise fash... | On Graph Neural Networks versus Graph-Augmented MLPs |
d260682557 | Large language models (LLMs) have demonstrated remarkable generalizability, such as understanding arbitrary entities and relations. Instruction tuning has proven effective for distilling LLMs into more cost-efficient models such as Alpaca and Vicuna. Yet such student models still trail the original LLMs by large margin... | UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition |
d258108073 | Recent works have shown that large models pretrained on common visual learning tasks can provide useful representations for a wide range of specialized perception problems, as well as a variety of robotic manipulation tasks. While prior work on robotic manipulation has predominantly used frozen pretrained features, we ... | LOSSLESS ADAPTATION OF PRETRAINED VISION MODELS FOR ROBOTIC MANIPULATION |
d235368285 | It is widely believed that deep neural networks contain layer specialization, wherein neural networks extract hierarchical features representing edges and patterns in shallow layers and complete objects in deeper layers. Unlike common feed-forward models that have distinct filters at each layer, recurrent networks reus... | THE UNCANNY SIMILARITY OF RECURRENCE AND DEPTH |
d53015479 | Credit assignment in Meta-reinforcement learning (Meta-RL) is still poorly understood. Existing methods either neglect credit assignment to pre-adaptation behavior or implement it naively. This leads to poor sample-efficiency during metatraining as well as ineffective task identification strategies. This paper provides... | PROMP: PROXIMAL META-POLICY SEARCH |
d257279878 | Recent works have shown that self-supervised learning can achieve remarkable robustness when integrated with adversarial training (AT). However, the robustness gap between supervised AT (sup-AT) and self-supervised AT (self-AT) remains significant. Motivated by this observation, we revisit existing self-AT methods and ... | RETHINKING THE EFFECT OF DATA AUGMENTATION IN ADVERSARIAL CONTRASTIVE LEARNING |
d219636414 | Designing task-oriented dialogue systems is a challenging research topic, since it needs not only to generate utterances fulfilling user requests but also to guarantee the comprehensibility. Many previous works trained end-to-end (E2E) models with supervised learning (SL), however, the bias in annotated system utteranc... | Modelling Hierarchical Structure between Dialogue Policy and Natural Language Generator with Option Framework for Task-oriented Dialogue System |
d15280949 | This paper proposes a new model for extracting an interpretable sentence embedding by introducing self-attention. Instead of using a vector, we use a 2-D matrix to represent the embedding, with each row of the matrix attending on a different part of the sentence. We also propose a self-attention mechanism and a special... | A STRUCTURED SELF-ATTENTIVE SENTENCE EMBEDDING |
d261959395 | Two lines of work are taking the central stage in AI research. On the one hand, the community is making increasing efforts to build models that discard spurious correlations and generalize better in novel test environments. Unfortunately, the bitter lesson so far is that no proposal convincingly outperforms a simple em... | Context is Environment |
d227255060 | Training a fair machine learning model is essential to prevent demographic disparity. Existing techniques for improving model fairness require broad changes in either data preprocessing or model training, rendering themselves difficult-to-adopt for potentially already complex machine learning systems. We address this p... | FAIRBATCH: BATCH SELECTION FOR MODEL FAIRNESS |
d222290992 | Mixup is a popular data augmentation technique based on taking convex combinations of pairs of examples and their labels. This simple technique has been shown to substantially improve both the robustness and the generalization of the trained model. However, it is not well-understood why such improvement occurs. In this... | How Does Mixup Help With Robustness and Generalization? |
d58006584 | Deep latent variable models have seen recent success in many data domains. Lossless compression is an application of these models which, despite having the potential to be highly useful, has yet to be implemented in a practical manner. We present 'Bits Back with ANS' (BB-ANS), a scheme to perform lossless compression w... | PRACTICAL LOSSLESS COMPRESSION WITH LATENT VARIABLES USING BITS BACK CODING |
d263671662 | We propose conditional flows of the maximum mean discrepancy (MMD) with the negative distance kernel for posterior sampling and conditional generative modelling.This MMD, which is also known as energy distance, has several advantageous properties like efficient computation via slicing and sorting.We approximate the joi... | Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernel |
d52908831 | A promising class of generative models maps points from a simple distribution to a complex distribution through an invertible neural network. Likelihood-based training of these models requires restricting their architectures to allow cheap computation of Jacobian determinants. Alternatively, the Jacobian trace can be u... | FFJORD: FREE-FORM CONTINUOUS DYNAMICS FOR SCALABLE REVERSIBLE GENERATIVE MODELS |
d229297705 | Matrix factorization is a simple and natural test-bed to investigate the implicit regularization of gradient descent. Gunasekar et al. (2018) conjectured that Gradient Flow with infinitesimal initialization converges to the solution that minimizes the nuclear norm, but a series of recent papers argued that the languag... | TOWARDS RESOLVING THE IMPLICIT BIAS OF GRADIENT DESCENT FOR MATRIX FACTORIZATION: GREEDY LOW-RANK LEARNING |
d256390439 | Many contrastive and meta-learning approaches learn representations by identifying common features in multiple views. However, the formalism for these approaches generally assumes features to be shared across views to be captured coherently. We consider the problem of learning a unified representation from partial obse... | CONTRASTIVE META-LEARNING FOR PARTIALLY OBSERVABLE FEW-SHOT LEARNING |
d233204737 | In recent years, great success has been witnessed in building problem-specific deep networks from unrolling iterative algorithms, for solving inverse problems and beyond. Unrolling is believed to incorporate the model-based prior with the learning capacity of deep learning. This paper revisits the role of unrolling as ... | A DESIGN SPACE STUDY FOR LISTA AND BEYOND |
d257912616 | An extension of Transformers is proposed that enables explicit relational reasoning through a novel module called the Abstractor.At the core of the Abstractor is a variant of attention called relational cross-attention.The approach is motivated by an architectural inductive bias for relational learning that disentangle... | Abstractors and relational cross-attention: An inductive bias for explicit relational reasoning in Transformers |
d52169956 | A wide range of defenses have been proposed to harden neural networks against adversarial attacks. However, a pattern has emerged in which the majority of adversarial defenses are quickly broken by new attacks. Given the lack of success at generating robust defenses, we are led to ask a fundamental question: Are advers... | ARE ADVERSARIAL EXAMPLES INEVITABLE? |
d253098210 | Future-or return-conditioned supervised learning is an emerging paradigm for offline reinforcement learning (RL), where the future outcome (i.e., return) associated with an observed action sequence is used as input to a policy trained to imitate those same actions. While return-conditioning is at the heart of popular a... | DICHOTOMY OF CONTROL: SEPARATING WHAT YOU CAN CONTROL FROM WHAT YOU CANNOT |
d248006483 | Pre-trained language models (LMs) have been shown to memorize a substantial amount of knowledge from the pre-training corpora; however, they are still limited in recalling factually correct knowledge given a certain context. Hence, they tend to suffer from counterfactual or hallucinatory generation when used in knowled... | KNOWLEDGE INFUSED DECODING |
d252692968 | We propose a new paradigm to help Large Language Models (LLMs) generate more accurate factual knowledge without retrieving from an external corpus, called RECITation-augmented gEneration (RECITE). Different from retrievalaugmented language models that retrieve relevant documents before generating the outputs, given an ... | RECITATION-AUGMENTED LANGUAGE MODELS |
d6550107 | A core aspect of human intelligence is the ability to learn new tasks quickly and switch between them flexibly. Here, we describe a modular continual reinforcement learning paradigm inspired by these abilities. We first introduce a visual interaction environment that allows many types of tasks to be unified in a single... | MODULAR CONTINUAL LEARNING IN A UNIFIED VISUAL ENVIRONMENT |
d252762275 | Large language models (LLMs) can perform complex reasoning by generating intermediate reasoning steps. Providing these steps for prompting demonstrations is called chain-of-thought (CoT) prompting. CoT prompting has two major paradigms. One leverages a simple prompt like "Let's think step by step" to facilitate step-by... | AUTOMATIC CHAIN OF THOUGHT PROMPTING IN LARGE LANGUAGE MODELS |
d202749994 | Likelihood-based generative models are a promising resource to detect out-of-distribution (OOD) inputs which could compromise the robustness or reliability of a machine learning system. However, likelihoods derived from such models have been shown to be problematic for detecting certain types of inputs that significant... | INPUT COMPLEXITY AND OUT-OF-DISTRIBUTION DETECTION WITH LIKELIHOOD-BASED GENERATIVE MODELS |
d238856644 | The success of deep ensembles on improving predictive performance, uncertainty estimation, and out-of-distribution robustness has been extensively studied in the machine learning literature. Albeit the promising results, naively training multiple deep neural networks and combining their predictions at inference leads t... | DEEP ENSEMBLING WITH NO OVERHEAD FOR EITHER TRAINING OR TESTING: THE ALL-ROUND BLESSINGS OF DYNAMIC SPARSITY |
d257913782 | Depth separation-why a deeper network is more powerful than a shallower onehas been a major problem in deep learning theory. Previous results often focus on representation power. For example, Safran et al. (2019) constructed a function that is easy to approximate using a 3-layer network but not approximable by any 2-la... | DEPTH SEPARATION WITH MULTILAYER MEAN-FIELD NETWORKS |
d234762955 | Top images were generated with seeds 5432 and 5431Generated images and extracted masksReal images and predicted masksFigure 1: We propose a method to automatically find a universal latent direction in a GAN that can separate the foreground from the background. We can then generate an unlimited supply of samples with ma... | Finding an Unsupervised Image Segmenter in Each of Your Deep Generative Models |
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