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d258833682 | In standard adversarial training, models are optimized to fit one-hot labels within allowable adversarial perturbation budgets. However, the ignorance of underlying distribution shifts brought by perturbations causes the problem of robust overfitting. To address this issue and enhance adversarial robustness, we analyze... | Annealing Self-Distillation Rectification Improves Adversarial Training |
d3511502 | Recent research has shown that one can train a neural network with binary weights and activations at train time by augmenting the weights with a high-precision continuous latent variable that accumulates small changes from stochastic gradient descent. However, there is a dearth of theoretical analysis to explain why we... | The High-Dimensional Geometry of Binary Neural Networks |
d262013578 | We explore the impact of parameter sparsity on the scaling behavior of Transformers trained on massive datasets (i.e., "foundation models"), in both vision and language domains.In this setting, we identify the first scaling law describing the relationship between weight sparsity, number of non-zero parameters, and amou... | SCALING LAWS FOR SPARSELY-CONNECTED FOUNDATION MODELS |
d247779010 | Machine learning models that achieve high overall accuracy often make systematic errors on important subsets (or slices) of data. Identifying underperforming slices is particularly challenging when working with high-dimensional inputs (e.g. images, audio), where important slices are often unlabeled. In order to address... | DOMINO: DISCOVERING SYSTEMATIC ERRORS WITH CROSS-MODAL EMBEDDINGS |
d222272028 | Continual (sequential) training and multitask (simultaneous) training are often attempting to solve the same overall objective: to find a solution that performs well on all considered tasks. The main difference is in the training regimes, where continual learning can only have access to one task at a time, which for ne... | LINEAR MODE CONNECTIVITY IN MULTITASK AND CONTINUAL LEARNING |
d252408526 | Modern machine learning research relies on relatively few carefully curated datasets. Even in these datasets, and typically in 'untidy' or raw data, practitioners are faced with significant issues of data quality and diversity which can be prohibitively labor intensive to address. Existing methods for dealing with thes... | Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics |
d32737310 | Softmax loss is widely used in deep neural networks for multi-class classification, where each class is represented by a weight vector, a sample is represented as a feature vector, and the feature vector has the largest projection on the weight vector of the correct category when the model correctly classifies a sample... | Feature Incay for Representation Regularization |
d247222761 | Drawing inspiration from gradient-based meta-learning methods with infinitely small gradient steps, we introduce Continuous-Time Meta-Learning (COMLN), a meta-learning algorithm where adaptation follows the dynamics of a gradient vector field. Specifically, representations of the inputs are meta-learned such that a tas... | CONTINUOUS-TIME META-LEARNING WITH FORWARD MODE DIFFERENTIATION |
d218889280 | Under mild regularity conditions, gradient-based methods converge globally to a critical point in the single-loss setting. This is known to break down for vanilla gradient descent when moving to multi-loss optimization, but can we hope to build some algorithm with global guarantees? We negatively resolve this open prob... | On the Impossibility of Global Convergence in Multi-Loss Optimization |
d249209577 | Strategic classification studies learning in settings where users can modify their features to obtain favorable predictions. Most current works focus on simple classifiers that trigger independent user responses. Here we examine the implications of learning with more elaborate models that break the independence assumpt... | STRATEGIC CLASSIFICATION WITH GRAPH NEURAL NETWORKS |
d259145304 | We investigate learning the equilibria in non-stationary multi-agent systems and address the challenges that differentiate multi-agent learning from single-agent learning. Specifically, we focus on games with bandit feedback, where testing an equilibrium can result in substantial regret even when the gap to be tested i... | A Black-box Approach for Non-stationary Multi-agent Reinforcement Learning |
d247292293 | There is increasing evidence suggesting neural networks' sensitivity to distribution shifts, so that research on out-of-distribution (OOD) generalization comes into the spotlight. Nonetheless, current endeavors mostly focus on Euclidean data, and its formulation for graph-structured data is not clear and remains under-... | HANDLING DISTRIBUTION SHIFTS ON GRAPHS: AN INVARIANCE PERSPECTIVE |
d258832670 | Text-driven diffusion models have unlocked unprecedented abilities in image generation, whereas their video counterpart still lags behind due to the excessive training cost of temporal modeling. Besides the training burden, the generated videos also suffer from appearance inconsistency and structural flickers, especial... | ControlVideo: Training-free Controllable Text-to-Video Generation |
d245334722 | Currently, it is hard to reap the benefits of deep learning for Bayesian methods, which allow the explicit specification of prior knowledge and accurately capture model uncertainty. We present Prior-Data Fitted Networks (PFNs). PFNs leverage large-scale machine learning techniques to approximate a large set of posterio... | TRANSFORMERS CAN DO BAYESIAN INFERENCE |
d209324424 | Lack of reliability is a well-known issue for reinforcement learning (RL) algorithms. This problem has gained increasing attention in recent years, and efforts to improve it have grown substantially. To aid RL researchers and production users with the evaluation and improvement of reliability, we propose a set of metri... | MEASURING THE RELIABILITY OF REINFORCEMENT LEARNING ALGORITHMS |
d260334759 | Despite the advancements of open-source large language models (LLMs), e.g., LLaMA, they remain significantly limited in tool-use capabilities, i.e., using external tools (APIs) to fulfill human instructions.The reason is that current instruction tuning largely focuses on basic language tasks but ignores the tool-use do... | TOOLLLM: FACILITATING LARGE LANGUAGE MODELS TO MASTER 16000+ REAL-WORLD APIS |
d202565538 | While most approaches to the problem of Inverse Reinforcement Learning (IRL) focus on estimating a reward function that best explains an expert agent's policy or demonstrated behavior on a control task, it is often the case that such behavior is more succinctly described by a simple reward combined with a set of hard c... | Maximum Likelihood Constraint Inference for Inverse Reinforcement Learning |
d67856178 | Modern neural networks are over-parametrized. In particular, each rectified linear hidden unit can be modified by a multiplicative factor by adjusting input and output weights, without changing the rest of the network. Inspired by the Sinkhorn-Knopp algorithm, we introduce a fast iterative method for minimizing the 2 n... | EQUI-NORMALIZATION OF NEURAL NETWORKS |
d204960684 | We introduce the Convolutional Conditional Neural Process (CONVCNP), a new member of the Neural Process family that models translation equivariance in the data. Translation equivariance is an important inductive bias for many learning problems including time series modelling, spatial data, and images. The model embeds ... | CONVOLUTIONAL CONDITIONAL NEURAL PROCESSES |
d208268589 | Consider an imitation learning problem that the imitator and the expert have different dynamics models. Most of the current imitation learning methods fail because they focus on imitating actions. We propose a novel state alignment based imitation learning method to train the imitator to follow the state sequences in e... | STATE ALIGNMENT-BASED IMITATION LEARNING |
d252280667 | Variational Autoencoder (VAE)-based generative models offer flexible representation learning by incorporating meta-priors, general premises considered beneficial for downstream tasks. However, the incorporated meta-priors often involve ad-hoc model deviations from the original likelihood architecture, causing undesirab... | GROMOV-WASSERSTEIN AUTOENCODERS |
d245131182 | Automatic speech recognition systems have created exciting possibilities for applications, however they also enable opportunities for systematic eavesdropping. We propose a method to camouflage a person's voice over-the-air from these systems without inconveniencing the conversation between people in the room. Standard... | REAL-TIME NEURAL VOICE CAMOUFLAGE |
d258480276 | Driven by large-data pre-training, Segment Anything Model (SAM) has been demonstrated as a powerful promptable framework, revolutionizing the segmentation field.Despite the generality, customizing SAM for specific visual concepts without man-powered prompting is under-explored, e.g., automatically segmenting your pet d... | PERSONALIZE SEGMENT ANYTHING MODEL WITH ONE SHOT |
d3484550 | Hierarchical reinforcement learning methods offer a powerful means of planning flexible behavior in complicated domains. However, learning an appropriate hierarchical decomposition of a domain into subtasks remains a substantial challenge. We present a novel algorithm for subtask discovery, based on the recently introd... | Hierarchical Subtask Discovery With Non-Negative Matrix Factorization |
d2721941 | Deep generative models have been successfully used to learn representations for high-dimensional discrete spaces by representing discrete objects as sequences and employing powerful sequence-based deep models. Unfortunately, these sequencebased models often produce invalid sequences: sequences which do not represent an... | LEARNING A GENERATIVE MODEL FOR VALIDITY IN COMPLEX DISCRETE STRUCTURES |
d203641746 | Recent theoretical work has established connections between over-parametrized neural networks and linearized models governed by the Neural Tangent Kernels (NTKs). NTK theory leads to concrete convergence and generalization results, yet the empirical performance of neural networks are observed to exceed their linearized... | Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks |
d11277821 | The Wasserstein distance received a lot of attention recently in the community of machine learning, especially for its principled way of comparing distributions. It has found numerous applications in several hard problems, such as domain adaptation, dimensionality reduction or generative models. However, its use is sti... | Learning Wasserstein Embeddings |
d261100669 | In this paper, we present a novel defense against backdoor attacks on deep neural networks (DNNs), wherein adversaries covertly implant malicious behaviors (backdoors) into DNNs.Our defense falls within the category of post-development defenses that operate independently of how the model was generated.Our proposed defe... | BADEXPERT: EXTRACTING BACKDOOR FUNCTIONALITY FOR ACCURATE BACKDOOR INPUT DETECTION |
d238198747 | We prove that Fp sketch, a well-celebrated streaming algorithm for frequency moments estimation, is differentially private as is when p ∈ (0, 1]. Fp sketch uses only polylogarithmic space, exponentially better than existing DP baselines and only worse than the optimal non-private baseline by a logarithmic factor. The e... | Differentially Private Fractional Frequency Moments Estimation with Polylogarithmic Space |
d252355382 | The task of building general agents that perform well over a wide range of tasks has been an important goal in reinforcement learning since its inception. The problem has been subject of research of a large body of work, with performance frequently measured by observing scores over the wide range of environments contai... | Human-level Atari 200x faster |
d222177165 | Prior AI breakthroughs in complex games have focused on either the purely adversarial or purely cooperative settings. In contrast, Diplomacy is a game of shifting alliances that involves both cooperation and competition. For this reason, Diplomacy has proven to be a formidable research challenge. In this paper we descr... | HUMAN-LEVEL PERFORMANCE IN NO-PRESS DIPLOMACY VIA EQUILIBRIUM SEARCH |
d249152222 | We study offline reinforcement learning (RL) in partially observable Markov decision processes. In particular, we aim to learn an optimal policy from a dataset collected by a behavior policy which possibly depends on the latent state. Such a dataset is confounded in the sense that the latent state simultaneously affect... | Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes |
d247011290 | When transferring a pretrained model to a downstream task, two popular methods are full fine-tuning (updating all the model parameters) and linear probing (updating only the last linear layer-the "head"). It is well known that fine-tuning leads to better accuracy in-distribution (ID). However, in this paper, we find th... | Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution |
d13046179 | We consider the two related problems of detecting if an example is misclassified or out-of-distribution. We present a simple baseline that utilizes probabilities from softmax distributions. Correctly classified examples tend to have greater maximum softmax probabilities than erroneously classified and out-of-distributi... | A BASELINE FOR DETECTING MISCLASSIFIED AND OUT-OF-DISTRIBUTION EXAMPLES IN NEURAL NETWORKS |
d254877510 | Fine-tuning pre-trained language models has become the prevalent paradigm for building downstream NLP models.Oftentimes fine-tuned models are readily available but their training data is not, due to data privacy or intellectual property concerns.This creates a barrier to fusing knowledge across individual models to yie... | DATALESS KNOWLEDGE FUSION BY MERGING WEIGHTS OF LANGUAGE MODELS |
d13298214 | Learning to navigate in complex environments with dynamic elements is an important milestone in developing AI agents. In this work we formulate the navigation question as a reinforcement learning problem and show that data efficiency and task performance can be dramatically improved by relying on additional auxiliary t... | LEARNING TO NAVIGATE IN COMPLEX ENVIRONMENTS |
d1257772 | Most existing machine learning classifiers are highly vulnerable to adversarial examples. An adversarial example is a sample of input data which has been modified very slightly in a way that is intended to cause a machine learning classifier to misclassify it. In many cases, these modifications can be so subtle that a ... | ADVERSARIAL EXAMPLES IN THE PHYSICAL WORLD |
d247778377 | Multiobjective combinatorial optimization (MOCO) problems can be found in many real-world applications. However, exactly solving these problems would be very challenging, particularly when they are NP-hard. Many handcrafted heuristic methods have been proposed to tackle different MOCO problems over the past decades. In... | PARETO SET LEARNING FOR NEURAL MULTI-OBJECTIVE COMBINATORIAL OPTIMIZATION |
d259274820 | Representation learning has been evolving from traditional supervised training to Contrastive Learning (CL) and Masked Image Modeling (MIM). Previous works have demonstrated their pros and cons in specific scenarios, i.e., CL and supervised pre-training excel at capturing longer-range global patterns and enabling bette... | Hybrid Distillation: Connecting Masked Autoencoders with Contrastive Learners |
d265037895 | Generative Adversarial Networks (GANs) are one of the most popular tools for learning complex high dimensional distributions. However, generalization properties of GANs have not been well understood. In this paper, we analyze the generalization of GANs in practical settings. We show that discriminators trained on discr... | IMPROVING GENERALIZATION AND STABILITY OF GENERATIVE ADVERSARIAL NETWORKS |
d56895453 | Learning when to communicate and doing that effectively is essential in multi-agent tasks. Recent works show that continuous communication allows efficient training with back-propagation in multiagent scenarios, but have been restricted to fullycooperative tasks. In this paper, we present Individualized Controlled Cont... | LEARNING WHEN TO COMMUNICATE AT SCALE IN MULTIAGENT COOPERATIVE AND COMPETITIVE TASKS |
d252917667 | A powerful framework for studying graphs is to consider them as geometric graphs: nodes are randomly sampled from an underlying metric space, and any pair of nodes is connected if their distance is less than a specified neighborhood radius. Currently, the literature mostly focuses on uniform sampling and constant neigh... | Unveiling the Sampling Density in Non-Uniform Geometric Graphs |
d257985378 | Pruning neural networks before training has received increasing interest due to its potential to reduce training time and memory. One popular method is to prune the connections based on a certain metric, but it is not entirely clear what metric is the best choice. Recent advances in neural tangent kernel (NTK) theory s... | NTK-SAP: IMPROVING NEURAL NETWORK PRUNING BY ALIGNING TRAINING DYNAMICS |
d263909549 | Self-supervised learning has unlocked the potential of scaling up pretraining to billions of images, since annotation is unnecessary.But are we making the best use of data?How more economical can we be?In this work, we attempt to answer this question by making two contributions.First, we investigate first-person videos... | IS IMAGENET WORTH 1 VIDEO? LEARNING STRONG IMAGE ENCODERS FROM 1 LONG UNLABELLED VIDEO |
d252780361 | We present 3DiM, a diffusion model for 3D novel view synthesis, which is able to translate a single input view into consistent and sharp completions across many views. The core component of 3DiM is a pose-conditional image-to-image diffusion model, which takes a source view and its pose as inputs, and generates a novel... | NOVEL VIEW SYNTHESIS WITH DIFFUSION MODELS |
d259212224 | The accurate modeling of dynamics in interactive environments is critical for successful long-range prediction. Such a capability could advance Reinforcement Learning (RL) and Planning algorithms, but achieving it is challenging. Inaccuracies in model estimates can compound, resulting in increased errors over long hori... | Efficient Dynamics Modeling in Interactive Environments with Koopman Theory |
d253255225 | Offline reinforcement learning (RL) struggles in environments with rich and noisy inputs, where the agent only has access to a fixed dataset without environment interactions. Past works have proposed common workarounds based on the pre-training of state representations, followed by policy training. In this work, we int... | BEHAVIOR PRIOR REPRESENTATION LEARNING FOR OFFLINE REINFORCEMENT LEARNING |
d252596234 | A zero-shot RL agent is an agent that can solve any RL task in a given environment, instantly with no additional planning or learning, after an initial reward-free learning phase. This marks a shift from the reward-centric RL paradigm towards "controllable" agents that can follow arbitrary instructions in an environmen... | Does Zero-Shot Reinforcement Learning Exist? |
d245005802 | For robots operating in the real world, it is desirable to learn reusable behaviours that can effectively be transferred and adapted to numerous tasks and scenarios. We propose an approach to learn abstract motor skills from data using a hierarchical mixture latent variable model. In contrast to existing work, our meth... | LEARNING TRANSFERABLE MOTOR SKILLS WITH HIERARCHICAL LATENT MIXTURE POLICIES |
d239016655 | A key challenge in neural architecture search (NAS) is quickly inferring the predictive performance of a broad spectrum of neural networks to discover statistically accurate and computationally efficient ones. We refer to this task as model performance inference (MPI). The current practice for efficient MPI is gradient... | GRADSIGN: MODEL PERFORMANCE INFERENCE WITH THEORETICAL INSIGHTS |
d262954661 | We investigate the internal behavior of Transformer-based Large Language Models (LLMs) when they generate factually incorrect text.We propose modeling factual queries as Constraint Satisfaction Problems and use this framework to investigate how the model interacts internally with factual constraints.Specifically, we di... | ATTENTION SATISFIES: A CONSTRAINT-SATISFACTION LENS ON FACTUAL ERRORS OF LANGUAGE MODELS |
d3476101 | The vast majority of natural sensory data is temporally redundant. Video frames or audio samples which are sampled at nearby points in time tend to have similar values. Typically, deep learning algorithms take no advantage of this redundancy to reduce computation. This can be an obscene waste of energy. We present a va... | Temporally Efficient Deep Learning with Spikes |
d232290577 | Searching for novel molecules with desired chemical properties is crucial in drug discovery. Existing work focuses on developing neural models to generate either molecular sequences or chemical graphs. However, it remains a big challenge to find novel and diverse compounds satisfying several properties. In this paper, ... | MARS: MARKOV MOLECULAR SAMPLING FOR MULTI-OBJECTIVE DRUG DISCOVERY |
d43964415 | Mini-batch stochastic gradient descent (SGD) is the state of the art in large scale parallel machine learning, but its scalability is limited by a communication bottleneck. Recent work proposed local SGD, i.e. running SGD independently in parallel on different workers and averaging only once in a while. This scheme sho... | Local SGD Converges Fast and Communicates Little |
d251765117 | Textual content is often the output of a collaborative writing process: We start with an initial draft, ask for suggestions, and repeatedly make changes. Agnostic of this process, today's language models are trained to generate only the final result. As a consequence, they lack several abilities crucial for collaborati... | PEER: A Collaborative Language Model |
d235254358 | Graph Attention Networks (GATs) are one of the most popular GNN architectures and are considered as the state-of-the-art architecture for representation learning with graphs. In GAT, every node attends to its neighbors given its own representation as the query. However, in this paper we show that GAT computes a very li... | HOW ATTENTIVE ARE GRAPH ATTENTION NETWORKS? |
d221470196 | Despite significant progress in challenging problems across various domains, applying state-of-the-art deep reinforcement learning (RL) algorithms remains challenging due to their sensitivity to the choice of hyperparameters. This sensitivity can partly be attributed to the non-stationarity of the RL problem, potential... | SAMPLE-EFFICIENT AUTOMATED DEEP REINFORCEMENT LEARNING |
d248986748 | Offline reinforcement learning algorithms still lack trust in practice due to the risk that the learned policy performs worse than the original policy that generated the dataset or behaves in an unexpected way that is unfamiliar to the user. At the same time, offline RL algorithms are not able to tune their most import... | User-Interactive Offline Reinforcement Learning |
d247450626 | The problem of molecular generation has received significant attention recently. Existing methods are typically based on deep neural networks and require training on large datasets with tens of thousands of samples. In practice, however, the size of class-specific chemical datasets is usually limited (e.g., dozens of s... | DATA-EFFICIENT GRAPH GRAMMAR LEARNING FOR MOLECULAR GENERATION |
d264289264 | Innovations like protein diffusion have enabled significant progress in de novo protein design, which is a vital topic in life science.These methods typically depend on protein structure encoders to model residue backbone frames, where atoms do not exist.Most prior encoders rely on atom-wise features, such as angles an... | DE NOVO PROTEIN DESIGN USING GEOMETRIC VECTOR FIELD NETWORKS |
d244527086 | Object-centric representations are a promising path toward more systematic generalization by providing flexible abstractions upon which compositional world models can be built. Recent work on simple 2D and 3D datasets has shown that models with object-centric inductive biases can learn to segment and represent meaningf... | CONDITIONAL OBJECT-CENTRIC LEARNING FROM VIDEO |
d258762142 | Subject-driven text-to-image generation aims to generate customized images of the given subject based on the text descriptions, which has drawn increasing attention recently. Existing methods mainly resort to finetuning a pretrained generative model, where the identity-relevant information and the identity-irrelevant i... | DisenBooth: Identity-Preserving Disentangled Tuning for Subject-Driven Text-to-Image Generation |
d46898034 | To optimize a neural network one often thinks of optimizing its parameters, but it is ultimately a matter of optimizing the function that maps inputs to outputs. Since a change in the parameters might serve as a poor proxy for the change in the function, it is of some concern that primacy is given to parameters but tha... | MEASURING AND REGULARIZING NETWORKS IN FUNCTION SPACE |
d203593355 | Training neural networks to be certifiably robust is a powerful defense against adversarial attacks. However, while promising, state-of-the-art results with certified training are far from satisfactory. Currently, it is very difficult to train a neural network that is both accurate and certified on realistic datasets a... | UNIVERSAL APPROXIMATION WITH CERTIFIED NETWORKS |
d108296236 | Euclidean embeddings of data are fundamentally limited in their ability to capture latent semantic structures, which need not conform to Euclidean spatial assumptions. Here we consider an alternative, which embeds data as discrete probability distributions in a Wasserstein space, endowed with an optimal transport metri... | LEARNING EMBEDDINGS INTO ENTROPIC WASSERSTEIN SPACES |
d246995268 | Simulation-based inference (SBI) refers to statistical inference on stochastic models for which we can generate samples, but not compute likelihoods. Like SBI algorithms, generative adversarial networks (GANs) do not require explicit likelihoods. We study the relationship between SBI and GANs, and introduce GATSBI, an ... | GATSBI: GENERATIVE ADVERSARIAL TRAINING FOR SIMULATION-BASED INFERENCE |
d258059971 | We present a method that enables synthesizing novel views and novel poses of arbitrary human performers from sparse multi-view images. A key ingredient of our method is a hybrid appearance blending module that combines the advantages of the implicit body NeRF representation and image-based rendering. Existing generaliz... | NEURAL IMAGE-BASED AVATARS: GENERALIZABLE RADIANCE FIELDS FOR HUMAN AVATAR MODELING |
d264435719 | This paper tackles two related questions at the heart of machine learning; how can we predict if a minimum will generalize to the test set, and why does stochastic gradient descent find minima that generalize well? Our work is inspired by Zhang et al. (2016), who showed deep networks can easily memorize randomly label... | A BAYESIAN PERSPECTIVE ON GENERALIZATION AND STOCHASTIC GRADIENT DESCENT |
d245704504 | Guided image synthesis enables everyday users to create and edit photo-realistic images with minimum effort. The key challenge is balancing faithfulness to the user inputs (e.g., hand-drawn colored strokes) and realism of the synthesized images. Existing GAN-based methods attempt to achieve such balance using either co... | SDEDIT: GUIDED IMAGE SYNTHESIS AND EDITING WITH STOCHASTIC DIFFERENTIAL EQUATIONS |
d219792208 | We solve the problem of 6-DoF localisation and 3D dense reconstruction in spatial environments as approximate Bayesian inference in a deep generative approach which combines learned with engineered models. This principled treatment of uncertainty and probabilistic inference overcomes the shortcoming of current state-of... | Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF |
d252185491 | Cross-silo Federated learning (FL) has become a promising tool in machine learning applications for healthcare. It allows hospitals/institutions to train models with sufficient data while the data is kept private. To make sure the FL model is robust when facing heterogeneous data among FL clients, most efforts focus on... | FedDAR: Federated Domain-Aware Representation Learning |
d220265858 | Neural network scaling has been critical for improving the model quality in many real-world machine learning applications with vast amounts of training data and compute. Although this trend of scaling is affirmed to be a sure-fire approach for better model quality, there are challenges on the path such as the computati... | GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding |
d263152829 | Large language models (LLMs) can "lie", which we define as outputting false statements despite "knowing" the truth in a demonstrable sense.LLMs might "lie", for example, when instructed to output misinformation.Here, we develop a simple lie detector that requires neither access to the LLM's activations (black-box) nor ... | HOW TO CATCH AN AI LIAR: LIE DETECTION IN BLACK-BOX LLMS BY ASKING UNRELATED QUESTIONS |
d3047732 | Most contemporary multi-task learning methods assume linear models. This setting is considered shallow in the era of deep learning. In this paper, we present a new deep multi-task representation learning framework that learns cross-task sharing structure at every layer in a deep network. Our approach is based on genera... | Deep Multi-task Representation Learning: A Tensor Factorisation Approach |
d233474778 | 3D convolution is powerful for video classification but often computationally expensive, recent studies mainly focus on decomposing it on spatial-temporal and/or channel dimensions. Unfortunately, most approaches fail to achieve a preferable balance between convolutional efficiency and feature-interaction sufficiency. ... | CT-NET: CHANNEL TENSORIZATION NETWORK FOR VIDEO CLASSIFICATION |
d247778993 | Text-based games (TBG) have emerged as promising environments for driving research in grounded language understanding and studying problems like generalization and sample efficiency. Several deep reinforcement learning (RL) methods with varying architectures and learning schemes have been proposed for TBGs. However, th... | CASE-BASED REASONING FOR BETTER GENERALIZATION IN TEXTUAL REINFORCEMENT LEARNING |
d208526932 | We study the phenomenon that some modules of deep neural networks (DNNs) are more critical than others. Meaning that rewinding their parameter values back to initialization, while keeping other modules fixed at the trained parameters, results in a large drop in the network's performance. Our analysis reveals interestin... | The intriguing role of module criticality in the generalization of deep networks |
d246822526 | Deployment efficiency is an important criterion for many real-world applications of reinforcement learning (RL). Despite the community's increasing interest, there lacks a formal theoretical formulation for the problem. In this paper, we propose such a formulation for deployment-efficient RL (DE-RL) from an "optimizati... | TOWARDS DEPLOYMENT-EFFICIENT REINFORCEMENT LEARNING: LOWER BOUND AND OPTIMALITY |
d202888876 | Value-based methods constitute a fundamental methodology in planning and deep reinforcement learning (RL). In this paper, we propose to exploit the underlying structures of the state-action value function, i.e., Q function, for both planning and deep RL. In particular, if the underlying system dynamics lead to some glo... | HARNESSING STRUCTURES FOR VALUE-BASED PLANNING AND REINFORCEMENT LEARNING |
d14711954 | We describe a framework for multitask deep reinforcement learning guided by policy sketches. Sketches annotate tasks with sequences of named subtasks, providing information about high-level structural relationships among tasks but not how to implement them-specifically not providing the detailed guidance used by much p... | Modular Multitask Reinforcement Learning with Policy Sketches |
d90258365 | As 3D scanning solutions become increasingly popular, several deep learning setups have been developed geared towards that task of scan completion, i.e., plausibly filling in regions there were missed in the raw scans. These methods, however, largely rely on supervision in the form of paired training data, i.e., partia... | Unpaired Point Cloud Completion on Real Scans using Adversarial Training |
d247849778 | The performance of existing text style transfer models is severely limited by the non-parallel datasets on which the models are trained. In non-parallel datasets, no direct mapping exists between sentences of the source and target style; the style transfer models thus only receive weak supervision of the target sentenc... | NON-PARALLEL TEXT STYLE TRANSFER WITH SELF-PARALLEL SUPERVISION |
d232257645 | Voice style transfer, also called voice conversion, seeks to modify one speaker's voice to generate speech as if it came from another (target) speaker.Previous works have made progress on voice conversion with parallel training data and pre-known speakers.However, zero-shot voice style transfer, which learns from non-p... | IMPROVING ZERO-SHOT VOICE STYLE TRANSFER VIA DISENTANGLED REPRESENTATION LEARNING |
d12130431 | This paper presents incremental network quantization (INQ), a novel method, targeting to efficiently convert any pre-trained full-precision convolutional neural network (CNN) model into a low-precision version whose weights are constrained to be either powers of two or zero. Unlike existing methods which are struggled ... | INCREMENTAL NETWORK QUANTIZATION: TOWARDS LOSSLESS CNNS WITH LOW-PRECISION WEIGHTS |
d3462549 | Deep autoregressive models have shown state-of-the-art performance in density estimation for natural images on large-scale datasets such as ImageNet. However, such models require many thousands of gradient-based weight updates and unique image examples for training. Ideally, the models would rapidly learn visual concep... | FEW-SHOT AUTOREGRESSIVE DENSITY ESTIMATION: TOWARDS LEARNING TO LEARN DISTRIBUTIONS |
d204824061 | State of the art sequence-to-sequence models for large scale tasks perform a fixed number of computations for each input sequence regardless of whether it is easy or hard to process. In this paper, we train Transformer models which can make output predictions at different stages of the network and we investigate differ... | DEPTH-ADAPTIVE TRANSFORMER |
d238419267 | Many practical problems need the output of a machine learning model to satisfy a set of constraints, K. There are, however, no known guarantees that classical neural networks can exactly encode constraints while simultaneously achieving universality. We provide a quantitative constrained universal approximation theorem... | UNIVERSAL APPROXIMATION UNDER CONSTRAINTS IS POSSIBLE WITH TRANSFORMERS |
d88517649 | VariationalAutoencoders (VAEs) provide a theoretically-backed framework for deep generative models. However, they often produce "blurry" images, which is linked to their training objective. Sampling in the most popular implementation, the Gaussian VAE, can be interpreted as simply injecting noise to the input of a dete... | From Variational to Deterministic Autoencoders |
d258741298 | By design, large language models (LLMs) are static general-purpose models, expensive to retrain or update frequently.As they are increasingly adopted for knowledge-intensive tasks, it becomes evident that these design choices lead to failures to generate factual, relevant, and up-to-date knowledge.To this end, we propo... | KNOWLEDGE CARD: FILLING LLMS' KNOWLEDGE GAPS WITH PLUG-IN SPECIALIZED LANGUAGE MODELS |
d252118863 | Masked autoencoders have become popular training paradigms for self-supervised visual representation learning. These models randomly mask a portion of the input and reconstruct the masked portion according to assigned target representations. In this paper, we show that a careful choice of the target representation is u... | Exploring Target Representations for Masked Autoencoders |
d259165410 | We revisit the problem of sampling from a target distribution that has a smooth strongly log-concave density everywhere in R p . In this context, if no additional density information is available, the randomized midpoint discretization for the kinetic Langevin diffusion is known to be the most scalable method in high d... | Langevin Monte Carlo for strongly log-concave distributions: Randomized midpoint revisited |
d238408085 | We introduce space-time graph neural network (ST-GNN), a novel GNN architecture, tailored to jointly process the underlying space-time topology of time-varying network data. The cornerstone of our proposed architecture is the composition of time and graph convolutional filters followed by pointwise nonlinear activation... | SPACE-TIME GRAPH NEURAL NETWORKS |
d264306022 | We present a novel variational framework for performing inference in (neural) stochastic differential equations (SDEs) driven by Markov-approximate fractional Brownian motion (fBM).SDEs offer a versatile tool for modeling real-world continuous-time dynamic systems with inherent noise and randomness.Combining SDEs with ... | VARIATIONAL INFERENCE FOR SDES DRIVEN BY FRACTIONAL NOISE |
d204907203 | We study the problem of model extraction in natural language processing, in which an adversary with only query access to a victim model attempts to reconstruct a local copy of that model. Assuming that both the adversary and victim model fine-tune a large pretrained language model such as BERT (Devlin et al., 2019), w... | THIEVES ON SESAME STREET! MODEL EXTRACTION OF BERT-BASED APIS |
d238408158 | A conventional approach to entity linking is to first find mentions in a given document and then infer their underlying entities in the knowledge base. A well-known limitation of this approach is that it requires finding mentions without knowing their entities, which is unnatural and difficult. We present a new model t... | ENTQA: ENTITY LINKING AS QUESTION ANSWERING |
d254096162 | Interpretability is a pressing issue for machine learning. Common approaches to interpretable machine learning constrain interactions between features of the input, rendering the effects of those features on a model's output comprehensible but at the expense of model complexity. We approach interpretability from a new ... | INTERPRETABILITY WITH FULL COMPLEXITY BY CONSTRAINING FEATURE INFORMATION |
d257482484 | Deformable object manipulation (DOM) is a long-standing challenge in robotics and has attracted significant interest recently. This paper presents DaXBench, a differentiable simulation framework for DOM. While existing work often focuses on a specific type of deformable objects, DaXBench supports fluid, rope, cloth . .... | DAXBENCH: BENCHMARKING DEFORMABLE OBJECT MANIPULATION WITH DIFFERENTIABLE PHYSICS |
d238407774 | Message-passing neural networks (MPNNs) are the leading architecture for deep learning on graph-structured data, in large part due to their simplicity and scalability. Unfortunately, it was shown that these architectures are limited in their expressive power. This paper proposes a novel framework called Equivariant Sub... | EQUIVARIANT SUBGRAPH AGGREGATION NETWORKS |
d252918265 | Self-training (ST), or pseudo-labeling has sparked significant interest in the automatic speech recognition (ASR) community recently because of its success in harnessing unlabeled data. Unlike prior semi-supervised learning approaches that relied on iteratively regenerating pseudo-labels (PLs) from a trained model and ... | CONTINUOUS PSEUDO-LABELING FROM THE START |
d240070972 | There has been emerging interest in using transductive learning for adversarial robustness (Goldwasser et al., NeurIPS 2020; Wu et al., ICML 2020; Wang et al., ArXiv 2021). Compared to traditional defenses, these defense mechanisms "dynamically learn" the model based on test-time input; and theoretically, attacking th... | TOWARDS EVALUATING THE ROBUSTNESS OF NEURAL NETWORKS LEARNED BY TRANSDUCTION |
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