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d229923250 | Encoder layer fusion (EncoderFusion) is a technique to fuse all the encoder layers (instead of the uppermost layer) for sequence-to-sequence (Seq2Seq) models, which has proven effective on various NLP tasks. However, it is still not entirely clear why and when EncoderFusion should work. In this paper, our main contribu... | UNDERSTANDING AND IMPROVING ENCODER LAYER FUSION IN SEQUENCE-TO-SEQUENCE LEARNING |
d212756 | Recurrent neural networks (RNNs), such as long short-term memory networks (LSTMs), serve as a fundamental building block for many sequence learning tasks, including machine translation, language modeling, and question answering. In this paper, we consider the specific problem of word-level language modeling and investi... | Regularizing and Optimizing LSTM Language Models |
d219969405 | Few-shot classification aims to recognize unseen classes when presented with only a small number of samples. We consider the problem of multi-domain few-shot image classification, where unseen classes and examples come from diverse data sources. This problem has seen growing interest and has inspired the development of... | A Universal Representation Transformer Layer for Few-Shot Image Classification |
d59279266 | We present graph wavelet neural network (GWNN), a novel graph convolutional neural network (CNN), leveraging graph wavelet transform to address the shortcomings of previous spectral graph CNN methods that depend on graph Fourier transform. Different from graph Fourier transform, graph wavelet transform can be obtained ... | GRAPH WAVELET NEURAL NETWORK |
d252815987 | Building on recent advances in image generation, we present a fully data-driven approach to rendering markup into images. The approach is based on diffusion models, which parameterize the distribution of data using a sequence of denoising operations on top of a Gaussian noise distribution. We view the diffusion denoisi... | MARKUP-TO-IMAGE DIFFUSION MODELS WITH SCHEDULED SAMPLING |
d209314627 | Uncertainty estimation and ensembling methods go hand-in-hand. Uncertainty estimation is one of the main benchmarks for assessment of ensembling performance. At the same time, deep learning ensembles have provided state-of-the-art results in uncertainty estimation. In this work, we focus on in-domain uncertainty for im... | PITFALLS OF IN-DOMAIN UNCERTAINTY ESTIMATION AND ENSEMBLING IN DEEP LEARNING |
d203737303 | Deep neural networks (DNNs) have been shown to over-fit a dataset when being trained with noisy labels for a long enough time. To overcome this problem, we present a simple and effective method self-ensemble label filtering (SELF) to progressively filter out the wrong labels during training. Our method improves the tas... | SELF: LEARNING TO FILTER NOISY LABELS WITH SELF-ENSEMBLING |
d222208810 | Given a simple request (e.g., Put a washed apple in the kitchen fridge), humans can reason in purely abstract terms by imagining action sequences and scoring their likelihood of success, prototypicality, and efficiency, all without moving a muscle.Once we see the kitchen in question, we can update our abstract plans to... | ALFWORLD: ALIGNING TEXT AND EMBODIED ENVIRONMENTS FOR INTERACTIVE LEARNING |
d255570226 | The combinatorial pure exploration of causal bandits is the following online learning task: given a causal graph with unknown causal inference distributions, in each round we choose a subset of variables to intervene or do no intervention, and observe the random outcomes of all random variables, with the goal that usin... | Combinatorial Pure Exploration of Causal Bandits |
d104292008 | Due to the lack of enough training data and high computational cost to train a deep neural network from scratch, transfer learning has been extensively used in many deep-neural-network-based applications, such as face recognition, image classification, speech recognition, etc. A commonly-used transfer learning approach... | A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer Learning |
d239024909 | Visual search, recommendation, and contrastive similarity learning power technologies that impact billions of users worldwide. Modern model architectures can be complex and difficult to interpret, and there are several competing techniques one can use to explain a search engine's behavior. We show that the theory of fa... | AXIOMATIC EXPLANATIONS FOR VISUAL SEARCH, RETRIEVAL, AND SIMILARITY LEARNING |
d260126025 | Robust reinforcement learning (RL) seeks to train policies that can perform well under environment perturbations or adversarial attacks. Existing approaches typically assume that the space of possible perturbations remains the same across timesteps. However, in many settings, the space of possible perturbations at a gi... | Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations |
d3524564 | We present an end-to-end trained memory system that quickly adapts to new data and generates samples like them. Inspired by Kanerva's sparse distributed memory, it has a robust distributed reading and writing mechanism. The memory is analytically tractable, which enables optimal on-line compression via a Bayesian updat... | THE KANERVA MACHINE: A GENERATIVE DISTRIBUTED MEMORY |
d226237047 | State-of-the-art natural language understanding classification models follow twostages: pre-training a large language model on an auxiliary task, and then finetuning the model on a task-specific labeled dataset using cross-entropy loss. Crossentropy loss has several shortcomings that can lead to sub-optimal generalizat... | SUPERVISED CONTRASTIVE LEARNING FOR PRE-TRAINED LANGUAGE MODEL FINE-TUNING |
d52900371 | The notion of the stationary equilibrium ensemble has played a central role in statistical mechanics. In machine learning as well, training serves as generalized equilibration that drives the probability distribution of model parameters toward stationarity. Here, we derive stationary fluctuation-dissipation relations t... | FLUCTUATION-DISSIPATION RELATIONS FOR STOCHASTIC GRADIENT DESCENT |
d226254365 | Effective training of deep neural networks can be challenging, and there remain many open questions on how to best learn these models. Recently developed methods to improve neural network training examine teaching: providing learned information during the training process to improve downstream model performance. In thi... | Teaching with Commentaries |
d245836975 | We present LSeg, a novel model for language-driven semantic image segmentation. LSeg uses a text encoder to compute embeddings of descriptive input labels (e.g., "grass" or "building") together with a transformer-based image encoder that computes dense per-pixel embeddings of the input image. The image encoder is train... | LANGUAGE-DRIVEN SEMANTIC SEGMENTATION |
d263829725 | Topology reasoning aims to comprehensively understand road scenes and present drivable routes in autonomous driving.It requires detecting road centerlines (lane) and traffic elements, further reasoning their topology relationship, i.e., lane-lane topology, and lane-traffic topology.In this work, we first present that t... | TOPOMLP: A SIMPLE YET STRONG PIPELINE FOR DRIVING TOPOLOGY REASONING |
d52890982 | While Generative Adversarial Networks (GANs) have seen wide success at the problem of synthesizing realistic images, they have seen little application to audio generation. Unlike for images, a barrier to success is that the best discriminative representations for audio tend to be non-invertible, and thus cannot be used... | ADVERSARIAL AUDIO SYNTHESIS |
d85459724 | As people learn to navigate the world, autonomic nervous system (e.g., "fight or flight") responses provide intrinsic feedback about the potential consequence of action choices (e.g., becoming nervous when close to a cliff edge or driving fast around a bend.) Physiological changes are correlated with these biological p... | VISCERAL MACHINES: RISK-AVERSION IN REINFORCEMENT LEARNING WITH INTRINSIC PHYSIOLOGICAL REWARDS |
d256416405 | In recent years numerous methods have been developed to formally verify the robustness of deep neural networks (DNNs). Though the proposed techniques are effective in providing mathematical guarantees about the DNNs behavior, it is not clear whether the proofs generated by these methods are human-interpretable. In this... | Interpreting Robustness Proofs of Deep Neural Networks |
d249097375 | Designing robust loss functions is popular in learning with noisy labels while existing designs did not explicitly consider the overfitting property of deep neural networks (DNNs). As a result, applying these losses may still suffer from overfitting/memorizing noisy labels as training proceeds. In this paper, we first ... | Mitigating Memorization of Noisy Labels via Regularization between Representations |
d189898449 | Network pruning is a promising avenue for compressing deep neural networks. A typical approach to pruning starts by training a model and then removing redundant parameters while minimizing the impact on what is learned. Alternatively, a recent approach shows that pruning can be done at initialization prior to training,... | A SIGNAL PROPAGATION PERSPECTIVE FOR PRUNING NEURAL NETWORKS AT INITIALIZATION |
d246442105 | Continuous-depth neural networks, such as Neural ODEs, have refashioned the understanding of residual neural networks in terms of non-linear vector-valued optimal control problems. The common solution is to use the adjoint sensitivity method to replicate a forward-backward pass optimisation problem. We propose a new ap... | IMBEDDING DEEP NEURAL NETWORKS |
d246607791 | In this paper, we study the representation of neural networks from the view of kernels. We first define the Neural Fisher Kernel (NFK), which is the Fisher Kernel (Jaakkola and Haussler, 1998) applied to neural networks. We show that NFK can be computed for both supervised and unsupervised learning models, which can se... | LEARNING REPRESENTATION FROM NEURAL FISHER KERNEL WITH LOW-RANK APPROXIMATION |
d257079072 | Discovering interpretable patterns for classification of sequential data is of key importance for a variety of fields, ranging from genomics to fraud detection or more generally interpretable decision-making. In this paper, we propose a novel differentiable fully interpretable method to discover both local and global p... | NEURAL-BASED CLASSIFICATION RULE LEARNING FOR SEQUENTIAL DATA |
d262083735 | Calibration measures and reliability diagrams are two fundamental tools for measuring and interpreting the calibration of probabilistic predictors.Calibration measures quantify the degree of miscalibration, and reliability diagrams visualize the structure of this miscalibration.However, the most common constructions of... | Smooth ECE: Principled Reliability Diagrams via Kernel Smoothing |
d253107476 | Despite the empirical advances of deep learning across a variety of learning tasks, our theoretical understanding of its success is still very restricted. One of the key challenges is the overparametrized nature of modern models, enabling complete overfitting of the data even if the labels are randomized, i.e. networks... | THE CURIOUS CASE OF BENIGN MEMORIZATION |
d220041969 | We propose a simple, practical, and intuitive approach for domain adaptation in reinforcement learning. Our approach stems from the idea that the agent's experience in the source domain should look similar to its experience in the target domain. Building off of a probabilistic view of RL, we formally show that we can a... | Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers |
d2808403 | We consider the use of Deep Learning methods for modeling complex phenomena like those occurring in natural physical processes. With the large amount of data gathered on these phenomena the data intensive paradigm could begin to challenge more traditional approaches elaborated over the years in fields like maths or phy... | Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge |
d220961494 | A recent line of work addresses the problem of predicting high-dimensional spatiotemporal phenomena by leveraging specific tools from the differential equations theory. Following this direction, we propose in this article a novel and general paradigm for this task based on a resolution method for partial differential e... | PDE-Driven Spatiotemporal Disentanglement |
d29154793 | We present a method for translating music across musical instruments, genres, and styles. This method is based on a multi-domain wavenet autoencoder, with a shared encoder and a disentangled latent space that is trained end-to-end on waveforms. Employing a diverse training dataset and large net capacity, the domain-ind... | A Universal Music Translation Network |
d247244739 | Machine learning classifiers with high test accuracy often perform poorly under adversarial attacks. It is commonly believed that adversarial training alleviates this issue. In this paper, we demonstrate that, surprisingly, the opposite may be true -Even though adversarial training helps when enough data is available, ... | Why adversarial training can hurt robust accuracy |
d8153918 | We study the problem of semantic code repair, which can be broadly defined as automatically fixing non-syntactic bugs in source code. The majority of past work in semantic code repair assumed access to unit tests against which candidate repairs could be validated. In contrast, the goal here is to develop a strong stati... | Semantic Code Repair using Neuro-Symbolic Transformation Networks |
d238419270 | Reinforcement learning encounters many challenges when applied directly in the real world. Sim-to-real transfer is widely used to transfer the knowledge learned from simulation to the real world. Domain randomization-one of the most popular algorithms for sim-to-real transfer-has been demonstrated to be effective in va... | UNDERSTANDING DOMAIN RANDOMIZATION FOR SIM-TO-REAL TRANSFER |
d222090711 | We present a novel view on principal component analysis (PCA) as a competitive game in which each approximate eigenvector is controlled by a player whose goal is to maximize their own utility function. We analyze the properties of this PCA game and the behavior of its gradient based updates. The resulting algorithm-whi... | EigenGame: PCA as a Nash Equilibrium |
d258686472 | Recent studies have shown that episodic reinforcement learning (RL) is no harder than bandits when the total reward is bounded by 1, and proved regret bounds that have a polylogarithmic dependence on the planning horizon H. However, it remains an open question that if such results can be carried over to adversarial RL,... | Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs |
d264812826 | Context-based fine-tuning methods, including prompting, in-context learning, soft prompting (also known as prompt tuning), and prefix-tuning, have gained popularity due to their ability to often match the performance of full fine-tuning with a fraction of the parameters.Despite their empirical successes, there is littl... | WHEN DO PROMPTING AND PREFIX-TUNING WORK? A THEORY OF CAPABILITIES AND LIMITATIONS |
d219558792 | Efficient training of deep neural networks is an increasingly important problem in the era of sophisticated architectures and large-scale datasets. This paper proposes a training set synthesis technique, called Dataset Condensation, that learns to produce a small set of informative samples for training deep neural netw... | Dataset Condensation with Gradient Matching |
d17220670 | External neural memory structures have recently become a popular tool for algorithmic deep learning(Graves et al., 2014;Weston et al., 2014). These models generally utilize differentiable versions of traditional discrete memory-access structures (random access, stacks, tapes) to provide the storage necessary for comput... | LIE-ACCESS NEURAL TURING MACHINES |
d257219732 | We consider cross-silo federated linear contextual bandit (LCB) problem under differential privacy, where multiple silos (agents) interact with the local users and communicate via a central server to realize collaboration while without sacrificing each user's privacy. We identify three issues in the state-of-the-art: (... | On Differentially Private Federated Linear Contextual Bandits |
d254854553 | Robustness evaluation against adversarial examples has become increasingly important to unveil the trustworthiness of the prevailing deep models in natural language processing (NLP). However, in contrast to the computer vision (CV) domain where the first-order projected gradient descent (PGD) is used as the benchmark a... | TEXTGRAD: ADVANCING ROBUSTNESS EVALUATION IN NLP BY GRADIENT-DRIVEN OPTIMIZATION |
d252531169 | Extractive summarization produces summaries by identifying and concatenating the most important sentences in a document. Since most summarization datasets do not come with gold labels indicating whether document sentences are summary-worthy, different labeling algorithms have been proposed to extrapolate oracle extract... | TEXT SUMMARIZATION WITH ORACLE EXPECTATION |
d245837268 | Reward hacking-where RL agents exploit gaps in misspecified reward functions-has been widely observed, but not yet systematically studied. To understand how reward hacking arises, we construct four RL environments with misspecified rewards. We investigate reward hacking as a function of agent capabilities: model capaci... | THE EFFECTS OF REWARD MISSPECIFICATION: MAPPING AND MITIGATING MISALIGNED MODELS |
d257078985 | In this paper, we propose energy-based sample adaptation at test time for domain generalization. Where previous works adapt their models to target domains, we adapt the unseen target samples to source-trained models. To this end, we design a discriminative energy-based model, which is trained on source domains to joint... | ENERGY-BASED TEST SAMPLE ADAPTATION FOR DOMAIN GENERALIZATION |
d20472740 | Natural Language Inference (NLI) task requires an agent to determine the logical relationship between a natural language premise and a natural language hypothesis. We introduce Interactive Inference Network (IIN), a novel class of neural network architectures that is able to achieve high-level understanding of the sent... | NATURAL LANGUAGE INFERENCE OVER INTERACTION SPACE |
d260498700 | Recurrent Neural Networks (RNN) are widely used to solve a variety of problems and as the quantity of data and the amount of available compute have increased, so have model sizes. The number of parameters in recent state-of-the-art networks makes them hard to deploy, especially on mobile phones and embedded devices. Th... | EXPLORING SPARSITY IN RECURRENT NEURAL NETWORKS |
d247594724 | Differentiable sorting algorithms allow training with sorting and ranking supervision, where only the ordering or ranking of samples is known. Various methods have been proposed to address this challenge, ranging from optimal transport-based differentiable Sinkhorn sorting algorithms to making classic sorting networks ... | MONOTONIC DIFFERENTIABLE SORTING NETWORKS |
d204734348 | We propose a novel algorithm for learning fair representations that can simultaneously mitigate two notions of disparity among different demographic subgroups in the classification setting. Two key components underpinning the design of our algorithm are balanced error rate and conditional alignment of representations. ... | CONDITIONAL LEARNING OF FAIR REPRESENTATIONS |
d1859294 | Recurrent Neural Networks (RNNs) continue to show outstanding performance in sequence modeling tasks. However, training RNNs on long sequences often face challenges like slow inference, vanishing gradients and difficulty in capturing long term dependencies. In backpropagation through time settings, these issues are tig... | Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks |
d249461627 | The performance of time series forecasting has recently been greatly improved by the introduction of transformers. In this paper, we propose a general multi-scale framework that can be applied to the state-of-the-art transformer-based time series forecasting models (FEDformer, Autoformer, etc.). By iteratively refining... | SCALEFORMER: ITERATIVE MULTI-SCALE REFINING TRANSFORMERS FOR TIME SERIES FORECASTING |
d204837843 | Graph Neural Networks (GNNs) are a powerful representational tool for solving problems on graph-structured inputs. In almost all cases so far, however, they have been applied to directly recovering a final solution from raw inputs, without explicit guidance on how to structure their problem-solving. Here, instead, we f... | NEURAL EXECUTION OF GRAPH ALGORITHMS |
d9655643 | Sequence-to-sequence models rely on a fixed decomposition of the target sequences into a sequence of tokens that may be words, word-pieces or characters. The choice of these tokens and the decomposition of the target sequences into a sequence of tokens is often static, and independent of the input, output data domains.... | LATENT SEQUENCE DECOMPOSITIONS |
d14992224 | We introduce Deep Variational Bayes Filters (DVBF), a new method for unsupervised learning of latent Markovian state space models. Leveraging recent advances in Stochastic Gradient Variational Bayes, DVBF can overcome intractable inference distributions by means of variational inference. Thus, it can handle highly nonl... | Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data |
d8728609 | Recent work has begun exploring neural acoustic word embeddings-fixeddimensional vector representations of arbitrary-length speech segments corresponding to words. Such embeddings are applicable to speech retrieval and recognition tasks, where reasoning about whole words may make it possible to avoid ambiguous sub-word... | MULTI-VIEW RECURRENT NEURAL ACOUSTIC WORD EMBEDDINGS |
d264490854 | Current tools for machine learning fairness only admit a limited range of fairness definitions and have seen little integration with automatic differentiation libraries, despite the central role these libraries play in modern machine learning pipelines.We introduce a framework of fairness regularization terms (FAIRRETs... | FAIRRET: A FRAMEWORK FOR DIFFERENTIABLE FAIRNESS REGULARIZATION TERMS |
d231847016 | We study the problem of how to construct a set of policies that can be composed together to solve a collection of reinforcement learning tasks. Each task is a different reward function defined as a linear combination of known features. We consider a specific class of policy compositions which we call set improving poli... | DISCOVERING A SET OF POLICIES FOR THE WORST CASE REWARD |
d49907212 | We study the problem of generating adversarial examples in a black-box setting in which only lossoracle access to a model is available. We introduce a framework that conceptually unifies much of the existing work on black-box attacks, and we demonstrate that the current state-of-the-art methods are optimal in a natural... | Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors |
d221655222 | In this work, we propose information laundering, a novel framework for enhancing model privacy. Unlike data privacy that concerns the protection of raw data information, model privacy aims to protect an already-learned model that is to be deployed for public use. The private model can be obtained from general learning ... | Information Laundering for Model Privacy |
d263310924 | Seminal research in the field of graph neural networks (GNNs) has revealed a direct correspondence between the expressive capabilities of GNNs and the kdimensional Weisfeiler-Leman (kWL) test, a widely-recognized method for verifying graph isomorphism. This connection has reignited interest in comprehending the specifi... | ON THE POWER OF THE WEISFEILER-LEMAN TEST FOR GRAPH MOTIF PARAMETERS |
d52895589 | Graph Neural Networks (GNNs) for representation learning of graphs broadly follow a neighborhood aggregation framework, where the representation vector of a node is computed by recursively aggregating and transforming feature vectors of its neighboring nodes. Many GNN variants have been proposed and have achieved state... | HOW POWERFUL ARE GRAPH NEURAL NETWORKS? |
d222379753 | All few-shot learning techniques must be pre-trained on a large, labeled "base dataset". In problem domains where such large labeled datasets are not available for pre-training (e.g., X-ray images), one must resort to pre-training in a different "source" problem domain (e.g., ImageNet), which can be very different from... | SELF-TRAINING FOR FEW-SHOT TRANSFER ACROSS EXTREME TASK DIFFERENCES |
d246210276 | Backdoor attacks (BAs) are an emerging threat to deep neural network classifiers. A victim classifier will predict to an attacker-desired target class whenever a test sample is embedded with the same backdoor pattern (BP) that was used to poison the classifier's training set. Detecting whether a classifier is backdoor ... | POST-TRAINING DETECTION OF BACKDOOR ATTACKS FOR TWO-CLASS AND MULTI-ATTACK SCENARIOS |
d263334074 | Despite the success of large language models (LLMs), the task of theorem proving still remains one of the hardest reasoning tasks that is far from being fully solved.Prior methods using language models have demonstrated promising results, but they still struggle to prove even middle school level theorems.One common lim... | LEGO-PROVER: NEURAL THEOREM PROVING WITH GROWING LIBRARIES |
d238198403 | Voice conversion is a common speech synthesis task which can be solved in different ways depending on a particular real-world scenario. The most challenging one often referred to as one-shot many-to-many voice conversion consists in copying target voice from only one reference utterance in the most general case when bo... | DIFFUSION-BASED VOICE CONVERSION WITH FAST MAXIMUM LIKELIHOOD SAMPLING SCHEME |
d52900202 | Brain-Machine Interfaces (BMIs) have recently emerged as a clinically viable option to restore voluntary movements after paralysis. These devices are based on the ability to extract information about movement intent from neural signals recorded using multi-electrode arrays chronically implanted in the motor cortices of... | ADVERSARIAL DOMAIN ADAPTATION FOR STABLE BRAIN-MACHINE INTERFACES |
d60441438 | Intelligent agents can learn to represent the action spaces of other agents simply by observing them act.Such representations help agents quickly learn to predict the effects of their own actions on the environment and to plan complex action sequences.In this work, we address the problem of learning an agent's action s... | LEARNING WHAT YOU CAN DO BEFORE DOING ANYTHING |
d219572891 | Learned Bloom filters enhance standard Bloom filters by using a learned model for the represented data set. However, a learned Bloom filter may under-utilize the model by not taking full advantage of the output. The learned Bloom filter uses the output score by simply applying a threshold, with elements above the thres... | Partitioned Learned Bloom Filters |
d264490454 | To correctly use in-context information, language models (LMs) must bind entities to their attributes.For example, given a context describing a "green square" and a "blue circle", LMs must bind the shapes to their respective colors.We analyze LM representations and identify the binding ID mechanism: a general mechanism... | HOW DO LANGUAGE MODELS BIND ENTITIES IN CONTEXT? |
d52893515 | We investigate the loss surface of neural networks. We prove that even for one-hidden-layer networks with "slightest" nonlinearity, the empirical risks have spurious local minima in most cases. Our results thus indicate that in general "no spurious local minima" is a property limited to deep linear networks, and insigh... | Small nonlinearities in activation functions create bad local minima in neural networks |
d51926976 | The ability to generate natural language sequences from source code snippets has a variety of applications such as code summarization, documentation, and retrieval. Sequence-to-sequence (seq2seq) models, adopted from neural machine translation (NMT), have achieved state-of-the-art performance on these tasks by treating... | CODE2SEQ: GENERATING SEQUENCES FROM STRUCTURED REPRESENTATIONS OF CODE |
d209977508 | This paper introduces Meta-Q-Learning (MQL), a new off-policy algorithm for meta-Reinforcement Learning (meta-RL). MQL builds upon three simple ideas. First, we show that Q-learning is competitive with state-of-the-art meta-RL algorithms if given access to a context variable that is a representation of the past traject... | META-Q-LEARNING |
d65455367 | Several recently proposed stochastic optimization methods that have been successfully used in training deep networks such as RMSPROP, ADAM, ADADELTA, NADAM are based on using gradient updates scaled by square roots of exponential moving averages of squared past gradients. In many applications, e.g. learning with large ... | ON THE CONVERGENCE OF ADAM AND BEYOND |
d3300406 | Options in reinforcement learning allow agents to hierarchically decompose a task into subtasks, having the potential to speed up learning and planning. However, autonomously learning effective sets of options is still a major challenge in the field. In this paper we focus on the recently introduced idea of using repre... | EIGENOPTION DISCOVERY THROUGH THE DEEP SUCCESSOR REPRESENTATION |
d219956317 | Modern large-scale machine learning applications require stochastic optimization algorithms to be implemented on distributed compute systems. A key bottleneck of such systems is the communication overhead for exchanging information across the workers, such as stochastic gradients. Among the many techniques proposed to ... | A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning |
d3720457 | Careful tuning of the learning rate, or even schedules thereof, can be crucial to effective neural net training. There has been much recent interest in gradient-based meta-optimization, where one tunes hyperparameters, or even learns an optimizer, in order to minimize the expected loss when the training procedure is un... | UNDERSTANDING SHORT-HORIZON BIAS IN STOCHASTIC META-OPTIMIZATION |
d263831046 | We tackle the challenge of large-scale network intervention for guiding excitatory point processes, such as infectious disease spread or traffic congestion control.Our model-based reinforcement learning utilizes neural ODEs to capture how the networked excitatory point processes will evolve subject to the time-varying ... | AMORTIZED NETWORK INTERVENTION TO STEER THE EXCITATORY POINT PROCESSES |
d257405190 | Figure 1. Our model supports photo-realistic large-hole inpainting for various scenarios. The first example for object removal is a highresolution image captured in the wild, while other inpainting examples (512 × 512) come from Places2 [82] and CelebA-HQ [23] datasets.AbstractGenerative adversarial networks (GANs) hav... | Image Inpainting via Iteratively Decoupled Probabilistic Modeling |
d3502468 | Incremental class learning involves sequentially learning classes in bursts of examples from the same class. This violates the assumptions that underlie methods for training standard deep neural networks, and will cause them to suffer from catastrophic forgetting. Arguably, the best method for incremental class learnin... | FEARNET: BRAIN-INSPIRED MODEL FOR INCREMENTAL LEARNING |
d235899205 | We introduce HTLM, a hyper-text language model trained on a large-scale web crawl. Modeling hyper-text has a number of advantages: (1) it is easily gathered at scale, (2) it provides rich document-level and end-taskadjacent supervision (e.g. class and id attributes often encode document category information), and (3) i... | HTLM: Hyper-Text Pre-Training and Prompting of Language Models |
d252668463 | Explaining generalizations and preventing over-confident predictions are central goals of studies on the loss landscape of neural networks. Flatness, defined as loss invariability on perturbations of a pre-trained solution, is widely accepted as a predictor of generalization in this context. However, the problem that f... | Scale-invariant Bayesian Neural Networks with Connectivity Tangent Kernel |
d173990564 | Neural Networks (NNs) have been extensively used for a wide spectrum of realworld regression tasks, where the goal is to predict a numerical outcome such as revenue, effectiveness, or a quantitative result. In many such tasks, the point prediction is not enough, but also the uncertainty (i.e. risk, or confidence) of th... | Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel |
d203734686 | The critical locus of the loss function of a neural network is determined by the geometry of the functional space and by the parameterization of this space by the network's weights. We introduce a natural distinction between pure critical points, which only depend on the functional space, and spurious critical points, ... | PURE AND SPURIOUS CRITICAL POINTS: A GEOMETRIC STUDY OF LINEAR NETWORKS |
d236170938 | Learning the structure of a causal graphical model using both observational and interventional data is a fundamental problem in many scientific fields. A promising direction is continuous optimization for score-based methods, which, however, require constrained optimization to enforce acyclicity or lack convergence gua... | EFFICIENT NEURAL CAUSAL DISCOVERY WITHOUT ACYCLICITY CONSTRAINTS |
d231648113 | Neural Architecture Search (NAS) is quickly becoming the standard methodology to design neural network models. However, NAS is typically compute-intensive because multiple models need to be evaluated before choosing the best one. To reduce the computational power and time needed, a proxy task is often used for evaluati... | ZERO-COST PROXIES FOR LIGHTWEIGHT NAS |
d47012356 | As Machine Learning (ML) gets applied to security-critical or sensitive domains, there is a growing need for integrity and privacy for outsourced ML computations. A pragmatic solution comes from Trusted Execution Environments (TEEs), which use hardware and software protections to isolate sensitive computations from the... | SLALOM: FAST, VERIFIABLE AND PRIVATE EXECUTION OF NEURAL NETWORKS IN TRUSTED HARDWARE |
d202573030 | Nowadays, deep neural networks (DNNs) have become the main instrument for machine learning tasks within a wide range of domains, including vision, NLP, and speech. Meanwhile, in an important case of heterogenous tabular data, the advantage of DNNs over shallow counterparts remains questionable. In particular, there is ... | NEURAL OBLIVIOUS DECISION ENSEMBLES FOR DEEP LEARNING ON TABULAR DATA |
d56475997 | The ability of a reinforcement learning (RL) agent to learn about many reward functions at the same time has many potential benefits, such as the decomposition of complex tasks into simpler ones, the exchange of information between tasks, and the reuse of skills. We focus on one aspect in particular, namely the ability... | UNIVERSAL SUCCESSOR FEATURES APPROXIMATORS |
d227337121 | Although spatio-temporal graph neural networks have achieved great empirical success in handling multiple correlated time series, they may be impractical in some real-world scenarios due to a lack of sufficient high-quality training data. Furthermore, spatio-temporal graph neural networks lack theoretical interpretatio... | SPATIO-TEMPORAL GRAPH SCATTERING TRANSFORM |
d13206339 | Knowledge bases (KB), both automatically and manually constructed, are often incomplete -many valid facts can be inferred from the KB by synthesizing existing information. A popular approach to KB completion is to infer new relations by combinatory reasoning over the information found along other paths connecting a pai... | GO FOR A WALK AND ARRIVE AT THE ANSWER: REASONING OVER PATHS IN KNOWLEDGE BASES USING REINFORCEMENT LEARNING |
d247058691 | We introduce an adaptive tree search algorithm, that can find high-scoring outputs under translation models that make no assumptions about the form or structure of the search objective. This algorithm -a deterministic variant of Monte Carlo tree search -enables the exploration of new kinds of models that are unencumber... | ENABLING ARBITRARY TRANSLATION OBJECTIVES WITH ADAPTIVE TREE SEARCH |
d208527270 | In Machine Learning as a Service, a provider trains a deep neural network and provides many users access to it. However, the hosted (source) model is susceptible to model stealing attacks where an adversary derives a surrogate model from API access to the source model. For post hoc detection of such attacks, the provid... | Deep Neural Network Fingerprinting by Conferrable Adversarial Examples |
d233254411 | Concept-based explanation approach is a popular model interpertability tool because it expresses the reasons for a model's predictions in terms of concepts that are meaningful for the domain experts. In this work, we study the problem of the concepts being correlated with confounding information in the features. We pro... | DEBIASING CONCEPT-BASED EXPLANATIONS WITH CAUSAL ANALYSIS |
d3481593 | Training deep neural networks requires many training samples, but in practice training labels are expensive to obtain and may be of varying quality, as some may be from trusted expert labelers while others might be from heuristics or other sources of weak supervision such as crowd-sourcing. This creates a fundamental q... | FIDELITY-WEIGHTED LEARNING |
d256627383 | In this work, we attempt to bridge the two fields of finite-agent and infinite-agent games, by studying how the optimal policies of agents evolve with the number of agents (population size) in mean-field games, an agent-centric perspective in contrast to the existing works focusing typically on the convergence of the e... | POPULATION-SIZE-AWARE POLICY OPTIMIZATION FOR MEAN-FIELD GAMES |
d257219926 | Soft threshold pruning is among the cutting-edge pruning methods with state-ofthe-art performance 1 . However, previous methods either perform aimless searching on the threshold scheduler or simply set the threshold trainable, lacking theoretical explanation from a unified perspective. In this work, we reformulate soft... | A UNIFIED FRAMEWORK FOR SOFT THRESHOLD PRUNING |
d59316477 | A fundamental question in reinforcement learning is whether model-free algorithms are sample efficient. Recently, Jin et al. [6] proposed a Q-learning algorithm with UCB exploration policy, and proved it has nearly optimal regret bound for finite-horizon episodic MDP. In this paper, we adapt Q-learning with UCB-explor... | Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP |
d202750230 | Overparameterized transformer networks have obtained state of the art results in various natural language processing tasks, such as machine translation, language modeling, and question answering. These models contain hundreds of millions of parameters, necessitating a large amount of computation and making them prone t... | REDUCING TRANSFORMER DEPTH ON DEMAND WITH STRUCTURED DROPOUT |
d11428611 | The concepts of unitary evolution matrices and associative memory have boosted the field of Recurrent Neural Networks (RNN) to state-of-the-art performance in a variety of sequential tasks. However, RNN still have a limited capacity to manipulate long-term memory. To bypass this weakness the most successful application... | ROTATIONAL UNIT OF MEMORY |
d203641721 | A key element of understanding the efficacy of overparameterized neural networks is characterizing how they represent functions as the number of weights in the network approaches infinity. In this paper, we characterize the norm required to realize a function f : R d → R as a single hidden-layer ReLU network with an un... | A FUNCTION SPACE VIEW OF BOUNDED NORM INFINITE WIDTH RELU NETS: THE MULTIVARIATE CASE |
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