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d36483539 | Model compression is significant for wide adoption of Recurrent Neural Networks (RNNs) in both user devices possessing limited resources and in business clusters requiring quick responses to large-scale service requests. In this work, we focus on reducing the sizes of basic structures (including input updates, gates, h... | LEARNING INTRINSIC SPARSE STRUCTURES WITHIN LONG SHORT-TERM MEMORY |
d256662499 | Reliability of machine learning evaluation -the consistency of observed evaluation scores across replicated model training runs -is affected by several sources of nondeterminism which can be regarded as measurement noise.Current tendencies to remove noise in order to enforce reproducibility of research results neglect ... | TOWARDS INFERENTIAL REPRODUCIBILITY OF MACHINE LEARNING RESEARCH |
d263333942 | The capacity of a modern deep learning system to determine if a sample falls within its realm of knowledge is fundamental and important.In this paper, we offer insights and analyses of recent state-of-the-art out-of-distribution (OOD) detection methods -extremely simple activation shaping (ASH).We demonstrate that acti... | SCALING FOR TRAINING TIME AND POST-HOC OUT-OF-DISTRIBUTION DETECTION ENHANCEMENT |
d249712234 | A major challenge in modern machine learning is theoretically understanding the generalization properties of overparameterized models. Many existing tools rely on uniform convergence (UC), a property that, when it holds, guarantees that the test loss will be close to the training loss, uniformly over a class of candida... | Max-Margin Works while Large Margin Fails: Generalization without Uniform Convergence |
d67749672 | Many machine learning algorithms represent input data with vector embeddings or discrete codes. When inputs exhibit compositional structure (e.g. objects built from parts or procedures from subroutines), it is natural to ask whether this compositional structure is reflected in the the inputs' learned representations. W... | MEASURING COMPOSITIONALITY IN REPRESENTATION LEARNING |
d85501306 | In this paper, we propose a residual non-local attention network for high-quality image restoration. Without considering the uneven distribution of information in the corrupted images, previous methods are restricted by local convolutional operation and equal treatment of spatial-and channel-wise features. To address t... | RESIDUAL NON-LOCAL ATTENTION NETWORKS FOR IMAGE RESTORATION |
d43933271 | Coding theory is a central discipline underpinning wireline and wireless modems that are the workhorses of the information age. Progress in coding theory is largely driven by individual human ingenuity with sporadic breakthroughs over the past century. In this paper we study whether it is possible to automate the disco... | Communication Algorithms via Deep Learning |
d29153681 | Adapting deep networks to new concepts from a few examples is challenging, due to the high computational requirements of standard fine-tuning procedures.Most work on few-shot learning has thus focused on simple learning techniques for adaptation, such as nearest neighbours or gradient descent. Nonetheless, the machine ... | META-LEARNING WITH DIFFERENTIABLE CLOSED-FORM SOLVERS |
d44065372 | The success of popular algorithms for deep reinforcement learning, such as policygradients and Q-learning, relies heavily on the availability of an informative reward signal at each timestep of the sequential decision-making process. When rewards are only sparsely available during an episode, or a rewarding feedback is... | LEARNING SELF-IMITATING DIVERSE POLICIES |
d232307426 | This paper introduces Relative Predictive Coding (RPC), a new contrastive representation learning objective that maintains a good balance among training stability, minibatch size sensitivity, and downstream task performance. The key to the success of RPC is two-fold. First, RPC introduces the relative parameters to reg... | SELF-SUPERVISED REPRESENTATION LEARNING WITH RELATIVE PREDICTIVE CODING |
d244908617 | Denoising diffusion probabilistic models have recently received much research attention since they outperform alternative approaches, such as GANs, and currently provide state-of-the-art generative performance. The superior performance of diffusion models has made them an appealing tool in several applications, includi... | LABEL-EFFICIENT SEMANTIC SEGMENTATION WITH DIFFUSION MODELS |
d253265328 | By conditioning on natural language instructions, large language models (LLMs) have displayed impressive capabilities as general-purpose computers. However, task performance depends significantly on the quality of the prompt used to steer the model, and most effective prompts have been handcrafted by humans. Inspired b... | LARGE LANGUAGE MODELS ARE HUMAN-LEVEL PROMPT ENGINEERS |
d238531695 | Transformers have made great progress in dealing with computer vision tasks.However, existing vision transformers do not yet possess the ability of building the interactions among features of different scales, which is perceptually important to visual inputs.The reasons are two-fold: (1) Input embeddings of each layer ... | CROSSFORMER: A VERSATILE VISION TRANSFORMER HINGING ON CROSS-SCALE ATTENTION |
d263830433 | Zero-shot learning in prompted vision-language models, the practice of crafting prompts to build classifiers without an explicit training process, has achieved impressive performance in many settings.This success presents a seemingly surprising observation: these methods suffer relatively little from overfitting, i.e.,... | Understanding prompt engineering may not require rethinking generalization |
d254408645 | Variational autoencoders (VAEs) are powerful tools for learning latent representations of data used in a wide range of applications. In practice, VAEs usually require multiple training rounds to choose the amount of information the latent variable should retain. This trade-off between the reconstruction error (distorti... | Multi-Rate VAE: Train Once, Get the Full Rate-Distortion Curve |
d3464416 | The problem of detecting whether a test sample is from in-distribution (i.e., training distribution by a classifier) or out-of-distribution sufficiently different from it arises in many real-world machine learning applications. However, the state-of-art deep neural networks are known to be highly overconfident in their... | TRAINING CONFIDENCE-CALIBRATED CLASSIFIERS FOR DETECTING OUT-OF-DISTRIBUTION SAMPLES |
d252693131 | Despite the tremendous success, existing machine learning models still fall short of human-like systematic generalization-learning compositional rules from limited data and applying them to unseen combinations in various domains. We propose Neural-Symbolic Recursive Machine (NSR) to tackle this deficiency. The core rep... | NEURAL-SYMBOLIC RECURSIVE MACHINE FOR SYSTEMATIC GENERALIZATION |
d235359206 | We consider sketching algorithms which first compress data by multiplication with a random sketch matrix, and then apply the sketch to quickly solve an optimization problem, e.g., low-rank approximation and regression. In the learning-based sketching paradigm proposed by [IVY19], the sketch matrix is found by choosing ... | Learning the Positions in CountSketch |
d209485520 | Optimistic initialisation is an effective strategy for efficient exploration in reinforcement learning (RL). In the tabular case, all provably efficient model-free algorithms rely on it. However, model-free deep RL algorithms do not use optimistic initialisation despite taking inspiration from these provably efficient ... | OPTIMISTIC EXPLORATION EVEN WITH A PESSIMISTIC INITIALISATION |
d67855732 | We propose a single neural probabilistic model based on variational autoencoder that can be conditioned on an arbitrary subset of observed features and then sample the remaining features in "one shot". The features may be both real-valued and categorical. Training of the model is performed by stochastic variational Bay... | VARIATIONAL AUTOENCODER WITH ARBITRARY CONDITIONING |
d256390109 | Despite the popularity and efficacy of knowledge distillation, there is limited understanding of why it helps. In order to study the generalization behavior of a distilled student, we propose a new theoretical framework that leverages supervision complexity: a measure of alignment between teacher-provided supervision a... | Supervision Complexity and its Role in Knowledge Distillation |
d221818900 | In this work, we propose DiffWave, a versatile Diffusion probabilistic model for conditional and unconditional Waveform generation. The model is nonautoregressive, and converts the white noise signal into structured waveform through a Markov chain with a constant number of steps at synthesis. It is efficiently trained ... | DIFFWAVE: A VERSATILE DIFFUSION MODEL FOR AUDIO SYNTHESIS |
d246430471 | In nature, the process of cellular growth and differentiation has lead to an amazing diversity of organisms -algae, starfish, giant sequoia, tardigrades, and orcas are all created by the same generative process. Inspired by the incredible diversity of this biological generative process, we propose a generative model, t... | VARIATIONAL NEURAL CELLULAR AUTOMATA |
d238583193 | We propose a new class of random feature methods for linearizing softmax and Gaussian kernels called hybrid random features (HRFs) that automatically adapt the quality of kernel estimation to provide most accurate approximation in the defined regions of interest. Special instantiations of HRFs lead to well-known method... | HYBRID RANDOM FEATURES |
d247996510 | The field of Natural Language Processing (NLP) has experienced a dramatic leap in capabilities with the recent introduction of huge Language Models (LMs). Despite this success, natural language problems that involve several compounded steps are still practically unlearnable, even by the largest LMs. This complies with ... | SUB-TASK DECOMPOSITION ENABLES LEARNING IN SEQUENCE TO SEQUENCE TASKS |
d226965123 | When designing controllers for safety-critical systems, practitioners often face a challenging tradeoff between robustness and performance. While robust control methods provide rigorous guarantees on system stability under certain worst-case disturbances, they often result in simple controllers that perform poorly in t... | Enforcing robust control guarantees within neural network policies |
d257279836 | Preference-based reinforcement learning (RL) provides a framework to train agents using human preferences between two behaviors.However, preferencebased RL has been challenging to scale since it requires a large amount of human feedback to learn a reward function aligned with human intent.In this paper, we present Pref... | PREFERENCE TRANSFORMER: MODELING HUMAN PREFERENCES USING TRANSFORMERS FOR RL |
d203838029 | The capability of making interpretable and self-explanatory decisions is essential for developing responsible machine learning systems. In this work, we study the learning to explain problem in the scope of inductive logic programming (ILP). We propose Neural Logic Inductive Learning (NLIL), an efficient differentiable... | LEARN TO EXPLAIN EFFICIENTLY VIA NEURAL LOGIC INDUCTIVE LEARNING |
d236087352 | A desirable property of autonomous agents is the ability to both solve long-horizon problems and generalize to unseen tasks. Recent advances in data-driven skill learning have shown that extracting behavioral priors from offline data can enable agents to solve challenging long-horizon tasks with reinforcement learning.... | HIERARCHICAL FEW-SHOT IMITATION WITH SKILL TRANSITION MODELS |
d257757426 | With the advent of large datasets, offline reinforcement learning (RL) is a promising framework for learning good decision-making policies without the need to interact with the real environment. However, offline RL requires the dataset to be reward-annotated, which presents practical challenges when reward engineering ... | OPTIMAL TRANSPORT FOR OFFLINE IMITATION LEARNING |
d239769065 | Goal-conditioned reinforcement learning (RL) can solve tasks in a wide range of domains, including navigation and manipulation, but learning to reach distant goals remains a central challenge to the field. Learning to reach such goals is particularly hard without any offline data, expert demonstrations, and reward shap... | C-PLANNING: AN AUTOMATIC CURRICULUM FOR LEARNING GOAL-REACHING TASKS |
d248810913 | A large number of studies that analyze deep neural network models and their ability to encode various linguistic and non-linguistic concepts provide an interpretation of the inner mechanics of these models. The scope of the analyses is limited to pre-defined concepts that reinforce the traditional linguistic knowledge ... | DISCOVERING LATENT CONCEPTS LEARNED IN BERT |
d52881014 | We present a meta-learning approach for adaptive text-to-speech (TTS) with few data. During training, we learn a multi-speaker model using a shared conditional WaveNet core and independent learned embeddings for each speaker. The aim of training is not to produce a neural network with fixed weights, which is then deplo... | SAMPLE EFFICIENT ADAPTIVE TEXT-TO-SPEECH |
d1091965 | Convolutional Neural Networks (CNNs) are computationally intensive, which limits their application on mobile devices. Their energy is dominated by the number of multiplies needed to perform the convolutions. Winograd's minimal filtering algorithm(Lavin, 2015)and network pruning(Han et al., 2015)can reduce the operation... | EFFICIENT SPARSE-WINOGRAD CONVOLUTIONAL NEURAL NETWORKS |
d218901036 | The vulnerability of deep networks to adversarial attacks is a central problem for deep learning from the perspective of both cognition and security. The current most successful defense method is to train a classifier using adversarial images created during learning. Another defense approach involves transformation or ... | Stochastic Security: Adversarial Defense Using Long-Run Dynamics of Energy-Based Models |
d237593074 | We present Pix2Seq, a simple and generic framework for object detection. Unlike existing approaches that explicitly integrate prior knowledge about the task, we cast object detection as a language modeling task conditioned on the observed pixel inputs. Object descriptions (e.g., bounding boxes and class labels) are exp... | PIX2SEQ: A LANGUAGE MODELING FRAMEWORK FOR OBJECT DETECTION |
d257079026 | Given a matrix M ∈ R m×n , the low rank matrix completion problem asks us to find a rank-k approximation of M as U V ⊤ for U ∈ R m×k and V ∈ R n×k by only observing a few entries specified by a set of entries Ω ⊆ [m] × [n]. In particular, we examine an approach that is widely used in practice -the alternating minimizat... | Low Rank Matrix Completion via Robust Alternating Minimization in Nearly Linear Time |
d238583808 | We study model-based offline Reinforcement Learning with general function approximation. We present an algorithm named Constrained Pessimistic Policy Optimization (CPPO) which leverages a general function class and uses a constraint to encode pessimism. Under the assumption that the ground truth model belongs to our fu... | Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage |
d235358707 | Self-supervised learning has recently shown great potential in vision tasks through contrastive learning, which aims to discriminate each image, or instance, in the dataset. However, such instance-level learning ignores the semantic relationship among instances and sometimes undesirably repels the anchor from the seman... | INCREMENTAL FALSE NEGATIVE DETECTION FOR CONTRASTIVE LEARNING |
d248377648 | In this work, we evaluate the effectiveness of representation learning approaches for decision making in visually complex environments. Representation learning is essential for effective reinforcement learning (RL) from high-dimensional inputs. Unsupervised representation learning approaches based on reconstruction, pr... | TASK-INDUCED REPRESENTATION LEARNING |
d210932183 | One-shot neural architecture search (NAS) has played a crucial role in making NAS methods computationally feasible in practice. Nevertheless, there is still a lack of understanding on how these weight-sharing algorithms exactly work due to the many factors controlling the dynamics of the process. In order to allow a sc... | NAS-BENCH-1SHOT1: BENCHMARKING AND DISSECTING ONE-SHOT NEURAL ARCHITECTURE SEARCH |
d49667227 | How can we build a learner that can capture the essence of what makes a hard problem more complex than a simple one, break the hard problem along characteristic lines into smaller problems it knows how to solve, and sequentially solve the smaller problems until the larger one is solved? To work towards this goal, we fo... | Automatically Composing Representation Transformations as a Means for Generalization |
d219792972 | Unsupervised meta-learning approaches rely on synthetic meta-tasks that are created using techniques such as random selection, clustering and/or augmentation. Unfortunately, clustering and augmentation are domain-dependent, and thus they require either manual tweaking or expensive learning. In this work, we describe an... | Unsupervised Meta-Learning through Latent-Space Interpolation in Generative Models |
d235352772 | We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that ar... | SPATIAL GRAPH ATTENTION AND CURIOSITY-DRIVEN POLICY FOR ANTIVIRAL DRUG DISCOVERY |
d253098484 | Many applications of quantum computing in the near term rely on variational quantum circuits (VQCs). They have been showcased as a promising model for reaching a quantum advantage in machine learning with current noisy intermediate scale quantum computers (NISQ). It is often believed that the power of VQCs relies on th... | Classically Approximating Variational Quantum Machine Learning with Random Fourier Features |
d256105646 | Federated Averaging (FedAvg) remains the most popular algorithm for Federated Learning (FL) optimization due to its simple implementation, stateless nature, and privacy guarantees combined with secure aggregation.Recent work has sought to generalize the vanilla averaging in FedAvg to a generalized gradient descent step... | FEDEXP: SPEEDING UP FEDERATED AVERAGING VIA EXTRAPOLATION |
d258988005 | We introduce a theoretical framework for sampling from unnormalized densities based on a smoothing scheme that uses an isotropic Gaussian kernel with a single fixed noise scale. We prove one can decompose sampling from a density (minimal assumptions made on the density) into a sequence of sampling from log-concave cond... | CHAIN OF LOG-CONCAVE MARKOV CHAINS |
d211171786 | The tremendous success of deep neural networks has motivated the need to better understand the fundamental properties of these networks, but many of the theoretical results proposed have only been for shallow networks. In this paper, we study an important primitive for understanding the meaningful input space of a deep... | Span Recovery for Deep Neural Networks with Applications to Input Obfuscation |
d221856753 | Gradient descent can be surprisingly good at optimizing deep neural networks without overfitting and without explicit regularization. We find that the discrete steps of gradient descent implicitly regularize models by penalizing gradient descent trajectories that have large loss gradients. We call this Implicit Gradien... | IMPLICIT GRADIENT REGULARIZATION |
d44005913 | The conventional solution to the recommendation problem greedily ranks individual document candidates by prediction scores. However, this method fails to optimize the slate as a whole, and hence, often struggles to capture biases caused by the page layout and document interdepedencies. The slate recommendation problem ... | BEYOND GREEDY RANKING: SLATE OPTIMIZATION VIA LIST-CVAE |
d235359192 | The noise in stochastic gradient descent (SGD), caused by minibatch sampling, is poorly understood despite its practical importance in deep learning. This work presents the first systematic study of the SGD noise and fluctuations close to a local minimum. We first analyze the SGD noise in linear regression in detail an... | STRENGTH OF MINIBATCH NOISE IN SGD |
d227054483 | Reinforcement learning provides a general framework for flexible decision making and control, but requires extensive data collection for each new task that an agent needs to learn. In other machine learning fields, such as natural language processing or computer vision, pre-training on large, previously collected datas... | PARROT: DATA-DRIVEN BEHAVIORAL PRIORS FOR REINFORCEMENT LEARNING |
d238531427 | Multimodal variational autoencoders (VAEs) have shown promise as efficient generative models for weakly-supervised data. Yet, despite their advantage of weak supervision, they exhibit a gap in generative quality compared to unimodal VAEs, which are completely unsupervised. In an attempt to explain this gap, we uncover ... | ON THE LIMITATIONS OF MULTIMODAL VAES |
d235390683 | Graph self-supervised learning has gained increasing attention due to its capacity to learn expressive node representations. Many pretext tasks, or loss functions have been designed from distinct perspectives. However, we observe that different pretext tasks affect downstream tasks differently across datasets, which su... | AUTOMATED SELF-SUPERVISED LEARNING FOR GRAPHS |
d244773230 | Complex sequential tasks in continuous-control settings often require agents to successfully traverse a set of "narrow passages" in their state space. Solving such tasks with a sparse reward in a sample-efficient manner poses a challenge to modern reinforcement learning (RL) due to the associated long-horizon nature of... | WISH YOU WERE HERE: HINDSIGHT GOAL SELECTION FOR LONG-HORIZON DEXTEROUS MANIPULATION |
d263828820 | The visual examination of tissue biopsy sections is fundamental for cancer diagnosis, with pathologists analyzing sections at multiple magnifications to discern tumor cells and their subtypes.However, existing attention-based multiple instance learning (MIL) models, used for analyzing Whole Slide Images (WSIs) in cance... | CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and Subtyping in Whole Slide Images |
d249538336 | Recent techniques for approximating Nash equilibria in very large games leverage neural networks to learn approximately optimal policies (strategies). One promising line of research uses neural networks to approximate counterfactual regret minimization (CFR) or its modern variants. DREAM, the only current CFR-based neu... | ESCHER: ESCHEWING IMPORTANCE SAMPLING IN GAMES BY COMPUTING A HISTORY VALUE FUNCTION TO ESTIMATE REGRET |
d52893258 | Compression is a key step to deploy large neural networks on resource-constrained platforms. As a popular compression technique, quantization constrains the number of distinct weight values and thus reducing the number of bits required to represent and store each weight. In this paper, we study the representation power... | ON THE UNIVERSAL APPROXIMABILITY AND COMPLEXITY BOUNDS OF QUANTIZED RELU NEURAL NETWORKS |
d54445324 | In complex transfer learning scenarios new tasks might not be tightly linked to previous tasks. Approaches that transfer information contained only in the final parameters of a source model will therefore struggle. Instead, transfer learning at a higher level of abstraction is needed. We propose Leap, a framework that ... | TRANSFERRING KNOWLEDGE ACROSS LEARNING PROCESSES |
d59599587 | Deep learning has achieved remarkable successes in solving challenging reinforcement learning (RL) problems when dense reward function is provided. However, in sparse reward environment it still often suffers from the need to carefully shape reward function to guide policy optimization. This limits the applicability of... | COMPETITIVE EXPERIENCE REPLAY |
d3290366 | We present graph partition neural networks (GPNN), an extension of graph neural networks (GNNs) able to handle extremely large graphs. GPNNs alternate between locally propagating information between nodes in small subgraphs and globally propagating information between the subgraphs. To efficiently partition graphs, we ... | Graph Partition Neural Networks for Semi-Supervised Classification |
d254926905 | Oversmoothing is a central challenge of building more powerful Graph Neural Networks (GNNs). While previous works have only demonstrated that oversmoothing is inevitable when the number of graph convolutions tends to infinity, in this paper, we precisely characterize the mechanism behind the phenomenon via a non-asympt... | A NON-ASYMPTOTIC ANALYSIS OF OVERSMOOTHING IN GRAPH NEURAL NETWORKS |
d235613482 | As a step towards improving the abstract reasoning capability of machines, we aim to solve Raven's Progressive Matrices (RPM) with neural networks, since solving RPM puzzles is highly correlated with human intelligence. Unlike previous methods that use auxiliary annotations or assume hidden rules to produce appropriate... | EFFECTIVE ABSTRACT REASONING WITH DUAL-CONTRAST NETWORK |
d249375375 | We study decentralized policy learning in Markov games where we control a single agent to play with nonstationary and possibly adversarial opponents. Our goal is to develop a no-regret online learning algorithm that (i) takes actions based on the local information observed by the agent and (ii) is able to find the best... | Decentralized Optimistic Hyperpolicy Mirror Descent: Provably No-Regret Learning in Markov Games |
d247922261 | Simplicial Embeddings (SEM) are representations learned through self-supervised learning (SSL), wherein a representation is projected into L simplices of V dimensions each using a softmax operation. This procedure conditions the representation onto a constrained space during pre-training and imparts an inductive bias f... | SIMPLICIAL EMBEDDINGS IN SELF-SUPERVISED LEARNING AND DOWNSTREAM CLASSIFICATION |
d222133257 | It is well-established that many iterative sparse reconstruction algorithms can be unrolled to yield a learnable neural network for improved empirical performance. A prime example is learned ISTA (LISTA) where weights, step sizes and thresholds are learned from training data. Recently, Analytic LISTA (ALISTA) has been ... | Neurally Augmented ALISTA |
d231698498 | Low-precision deep neural network (DNN) training has gained tremendous attention as reducing precision is one of the most effective knobs for boosting DNNs' training time/energy efficiency. In this paper, we attempt to explore low-precision training from a new perspective as inspired by recent findings in understanding... | CPT: EFFICIENT DEEP NEURAL NETWORK TRAINING VIA CYCLIC PRECISION |
d222272463 | We consider the question: how can you sample good negative examples for contrastive learning? We argue that, as with metric learning, learning contrastive representations benefits from hard negative samples (i.e., points that are difficult to distinguish from an anchor point). The key challenge toward using hard negati... | Contrastive Learning with Hard Negative Samples |
d259298217 | Out-of-distribution (OOD) generalization is a challenging machine learning problem yet highly desirable in many high-stake applications. Existing methods suffer from overly pessimistic modeling with low generalization confidence. As generalizing to arbitrary test distributions is impossible, we hypothesize that further... | TOPOLOGY-AWARE ROBUST OPTIMIZATION FOR OUT-OF-DISTRIBUTION GENERALIZATION |
d239768514 | Humans are expert explorers and foragers. Understanding the computational cognitive mechanisms that support this capability can advance the study of the human mind and enable more efficient exploration algorithms. We hypothesize that humans explore new environments by inferring the structure of unobserved spaces throug... | MAP INDUCTION: COMPOSITIONAL SPATIAL SUBMAP LEARNING FOR EFFICIENT EXPLORATION IN NOVEL ENVIRONMENTS |
d231719359 | For infinitesimal learning rates, stochastic gradient descent (SGD) follows the path of gradient flow on the full batch loss function. However moderately large learning rates can achieve higher test accuracies, and this generalization benefit is not explained by convergence bounds, since the learning rate which maximiz... | ON THE ORIGIN OF IMPLICIT REGULARIZATION IN STOCHASTIC GRADIENT DESCENT |
d252531851 | Learning to optimize is a rapidly growing area that aims to solve optimization problems or improve existing optimization algorithms using machine learning (ML). In particular, the graph neural network (GNN) is considered a suitable ML model for optimization problems whose variables and constraints are permutation-invar... | ON REPRESENTING LINEAR PROGRAMS BY GRAPH NEURAL NETWORKS |
d257496038 | Deep neural networks have been successful in many reinforcement learning settings. However, compared to human learners they are overly data hungry. To build a sample-efficient world model, we apply a transformer to real-world episodes in an autoregressive manner: not only the compact latent states and the taken actions... | TRANSFORMER-BASED WORLD MODELS ARE HAPPY WITH 100K INTERACTIONS |
d246996551 | This paper proposes a novel method for deep learning based on the analytical convolution of multidimensional Gaussian mixtures. In contrast to tensors, these do not suffer from the curse of dimensionality and allow for a compact representation, as data is only stored where details exist. Convolution kernels and data ar... | GAUSSIAN MIXTURE CONVOLUTION NETWORKS |
d3473020 | Localization is the problem of estimating the location of an autonomous agent from an observation and a map of the environment. Traditional methods of localization, which filter the belief based on the observations, are sub-optimal in the number of steps required, as they do not decide the actions taken by the agent. W... | ACTIVE NEURAL LOCALIZATION |
d53502621 | Neural network quantization is becoming an industry standard to efficiently deploy deep learning models on hardware platforms, such as CPU, GPU, TPU, and FPGAs. However, we observe that the conventional quantization approaches are vulnerable to adversarial attacks. This paper aims to raise people's awareness about the ... | DEFENSIVE QUANTIZATION: WHEN EFFICIENCY MEETS ROBUSTNESS |
d52917627 | Real-life control tasks involve matter of various substances-rigid or soft bodies, liquid, gas-each with distinct physical behaviors. This poses challenges to traditional rigid-body physics engines. Particle-based simulators have been developed to model the dynamics of these complex scenes; however, relying on approxim... | LEARNING PARTICLE DYNAMICS FOR MANIPULATING RIGID BODIES, DEFORMABLE OBJECTS, AND FLUIDS |
d252683860 | We study the problem of deployment efficient reinforcement learning (RL) with linear function approximation under the reward-free exploration setting. This is a well-motivated problem because deploying new policies is costly in real-life RL applications. Under the linear MDP setting with feature dimension d and plannin... | Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function Approximation |
d239616181 | Although DALL·E has shown an impressive ability of composition-based systematic generalization in image generation, it requires the dataset of text-image pairs and the compositionality is provided by the text. In contrast, object-centric representation models like the Slot Attention model learn composable representatio... | ILLITERATE DALL-E LEARNS TO COMPOSE |
d252668432 | We show that the representation cost of fully connected neural networks with homogeneous nonlinearities -which describes the implicit bias in function space of networks with L 2 -regularization or with losses such as the cross-entropy -converges as the depth of the network goes to infinity to a notion of rank over nonl... | IMPLICIT BIAS OF LARGE DEPTH NETWORKS: A NOTION OF RANK FOR NONLINEAR FUNCTIONS |
d53332150 | There is significant past and ongoing research on optimization methods for deep learning. Yet, perhaps surprisingly, there is no generally agreed-upon protocol for the quantitative and reproducible evaluation of such optimizers. We suggest routines and benchmarks for stochastic optimization, with special focus on the u... | DEEPOBS: A DEEP LEARNING OPTIMIZER BENCHMARK SUITE |
d263606004 | Diffusion models have recently been shown to be relevant for high-quality speech generation.Most work has been focused on generating spectrograms, and as such, they further require a subsequent model to convert the spectrogram to a waveform (i.e., a vocoder).This work proposes a diffusion probabilistic end-to-end model... | DIFFAR: DENOISING DIFFUSION AUTOREGRESSIVE MODEL FOR RAW SPEECH WAVEFORM GENERATION |
d248177810 | The most significant barrier to the advancement of Neural Architecture Search (NAS) is its demand for large computational resources, which hinders scientifically sound empirical evaluations of NAS methods. Tabular NAS benchmarks have alleviated this problem substantially, making it possible to properly evaluate NAS met... | SURROGATE NAS BENCHMARKS: GOING BEYOND THE LIMITED SEARCH SPACES OF TABULAR NAS BENCHMARKS |
d261494012 | Recent progress in large language models (LLMs) has enabled the processing of long texts consisting of tens of thousands of tokens, which are sufficient for numerous conventional NLP tasks. Many LLMs are trained/fine-tuned to perform zero-shot/few-shot inference using instruction-based prompts. Crafting prompts for the... | BATCHPROMPT: ACCOMPLISH MORE WITH LESS |
d227162606 | Continual learning deals with training models on new tasks and datasets in an online fashion. One strand of research has used probabilistic regularization for continual learning, with two of the main approaches in this vein being Online Elastic Weight Consolidation (Online EWC) and Variational Continual Learning (VCL).... | GENERALIZED VARIATIONAL CONTINUAL LEARNING |
d247475741 | We propose a new differentiable probabilistic model over DAGs (DP-DAG). DP-DAG allows fast and differentiable DAG sampling suited to continuous optimization. To this end, DP-DAG samples a DAG by successively (1) sampling a linear ordering of the node and (2) sampling edges consistent with the sampled linear ordering. W... | DIFFERENTIABLE DAG SAMPLING |
d232013402 | Graph neural networks have been widely used on modeling graph data, achieving impressive results on node classification and link prediction tasks. Yet, obtaining an accurate representation for a graph further requires a pooling function that maps a set of node representations into a compact form. A simple sum or averag... | ACCURATE LEARNING OF GRAPH REPRESENTATIONS WITH GRAPH MULTISET POOLING |
d264288929 | We study online reinforcement learning in linear Markov decision processes with adversarial losses and bandit feedback, without prior knowledge on transitions or access to simulators.We introduce two algorithms that achieve improved regret performance compared to existing approaches.The first algorithm, although comput... | Towards Optimal Regret in Adversarial Linear MDPs with Bandit Feedback |
d258999478 | Recent advancements in neural vocoding are predominantly driven by Generative Adversarial Networks (GANs) operating in the time-domain.While effective, this approach neglects the inductive bias offered by time-frequency representations, resulting in reduntant and computionally-intensive upsampling operations.Fourierbas... | VOCOS: CLOSING THE GAP BETWEEN TIME-DOMAIN AND FOURIER-BASED NEURAL VOCODERS FOR HIGH-QUALITY AUDIO SYNTHESIS |
d264436566 | GHOST ON THE SHELL: AN EXPRESSIVE REPRESENTATION OF GENERAL 3D SHAPES | |
d219708319 | Robotic manipulation tasks, such as wiping with a soft sponge, require control from multiple rich sensory modalities. Human-robot interaction, aimed at teaching robots, is difficult in this setting as there is potential for mismatch between human and machine comprehension of the rich data streams. We treat the task of ... | Learning from Demonstration with Weakly Supervised Disentanglement |
d231986303 | Assessing the complexity of functions computed by a neural network helps us understand how the network will learn and generalize. One natural measure of complexity is how the network distorts length -if the network takes a unit-length curve as input, what is the length of the resulting curve of outputs? It has been wid... | Deep ReLU Networks Preserve Expected Length |
d259937490 | Diffusion models have recently emerged as powerful generative priors for solving inverse problems.However, training diffusion models in the pixel space are both data-intensive and computationally demanding, which restricts their applicability as priors for high-dimensional real-world data such as medical images.Latent ... | Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency |
d252873172 | Without the demand of training in reality, humans can easily detect a known concept simply based on its language description. Empowering deep learning with this ability undoubtedly enables the neural network to handle complex vision tasks, e.g., object detection, without collecting and annotating real images. To this e... | IMAGINARYNET: LEARNING OBJECT DETECTORS WITHOUT REAL IMAGES AND ANNOTATIONS |
d220936168 | We study the global convergence and global optimality of actor-critic, one of the most popular families of reinforcement learning algorithms. While most existing works on actor-critic employ bi-level or two-timescale updates, we focus on the more practical single-timescale setting, where the actor and critic are update... | Single-Timescale Actor-Critic Provably Finds Globally Optimal Policy |
d258557287 | Diffusion models have emerged as a key pillar of foundation models in visual domains. One of their critical applications is to universally solve different downstream inverse tasks via a single diffusion prior without re-training for each task. Most inverse tasks can be formulated as inferring a posterior distribution o... | A VARIATIONAL PERSPECTIVE ON SOLVING INVERSE PROBLEMS WITH DIFFUSION MODELS |
d3463660 | We build a virtual agent for learning language in a 2D maze-like world. The agent sees images of the surrounding environment, listens to a virtual teacher, and takes actions to receive rewards. It interactively learns the teacher's language from scratch based on two language use cases: sentence-directed navigation and ... | INTERACTIVE GROUNDED LANGUAGE ACQUISITION AND GENERALIZATION IN A 2D WORLD |
d211296452 | We consider the task of answering complex multi-hop questions using a corpus as a virtual knowledge base (KB). In particular, we describe a neural module, DrKIT, that traverses textual data like a KB, softly following paths of relations between mentions of entities in the corpus. At each step the module uses a combinat... | DIFFERENTIABLE REASONING OVER A VIRTUAL KNOWLEDGE BASE |
d246823969 | 1-bit gradient compression and local steps are two representative techniques that enable drastic communication reduction in distributed SGD. Their benefits, however, remain an open question on Adambased large model pre-training (e.g. BERT and GPT). In this paper, we demonstrate the non-linearity in Adam causes slow con... | Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam |
d53732884 | A growing number of learning methods are actually differentiable games whose players optimise multiple, interdependent objectives in parallel -from GANs and intrinsic curiosity to multi-agent RL. Opponent shaping is a powerful approach to improve learning dynamics in these games, accounting for player influence on othe... | STABLE OPPONENT SHAPING IN DIFFERENTIABLE GAMES |
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