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d16299141 | Recent neural network sequence models with softmax classifiers have achieved their best language modeling performance only with very large hidden states and large vocabularies. Even then they struggle to predict rare or unseen words even if the context makes the prediction unambiguous. We introduce the pointer sentinel... | Pointer Sentinel Mixture Models |
d252735240 | Meta-training, which fine-tunes the language model (LM) on various downstream tasks by maximizing the likelihood of the target label given the task instruction and input instance, has improved the zero-shot task generalization performance. However, meta-trained LMs still struggle to generalize to challenging tasks cont... | GUESS THE INSTRUCTION! FLIPPED LEARNING MAKES LANGUAGE MODELS STRONGER ZERO-SHOT LEARNERS |
d252595881 | This work introduces DiGress, a discrete denoising diffusion model for generating graphs with categorical node and edge attributes. Our model utilizes a discrete diffusion process that progressively edits graphs with noise, through the process of adding or removing edges and changing the categories. A graph transformer... | DIGRESS: DISCRETE DENOISING DIFFUSION FOR GRAPH GENERATION |
d17299045 | Network quantization is one of network compression techniques employed to reduce the redundancy of deep neural networks. It compresses the size of the storage for a large number of network parameters in a neural network by quantizing them and encoding the quantized values into binary codewords of smaller sizes. In this... | TOWARDS THE LIMIT OF NETWORK QUANTIZATION |
d10135357 | 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 |
d235446639 | We consider the problem of estimating the number of distinct elements in a large data set (or, equivalently, the support size of the distribution induced by the data set) from a random sample of its elements. The problem occurs in many applications, including biology, genomics, computer systems and linguistics. A line ... | LEARNING-BASED SUPPORT ESTIMATION IN SUBLINEAR TIME |
d3986339 | Real-valued word representations have transformed NLP applications; popular examples are word2vec and GloVe, recognized for their ability to capture linguistic regularities. In this paper, we demonstrate a very simple, and yet counterintuitive, postprocessing technique -eliminate the common mean vector and a few top do... | All-but-the-Top: Simple and Effective Postprocessing for Word Representations |
d202734152 | Cloud computing is becoming increasingly popular as a platform for distributed training of deep neural networks. | GAP-AWARE MITIGATION OF GRADIENT STALENESS |
d257636846 | Continual learning is a problem for artificial neural networks that their biological counterparts are adept at solving. Building on work using Sparse Distributed Memory (SDM) to connect a core neural circuit with the powerful Transformer model, we create a modified Multi-Layered Perceptron (MLP) that is a strong contin... | SPARSE DISTRIBUTED MEMORY IS A CONTINUAL LEARNER |
d235829401 | Most existing Vision-and-Language (V&L) models rely on pre-trained visual encoders, using a relatively small set of manuallyannotated data (as compared to web-crawled data), to perceive the visual world. However, it has been observed that large-scale pretraining usually can result in better generalization performance, ... | How Much Can CLIP Benefit Vision-and-Language Tasks? |
d247518670 | We propose a new method for spatio-temporal forecasting on arbitrarily distributed points. Assuming that the observed system follows an unknown partial differential equation, we derive a continuous-time model for the dynamics of the data via the finite element method. The resulting graph neural network estimates the in... | LEARNING THE DYNAMICS OF PHYSICAL SYSTEMS FROM SPARSE OBSERVATIONS WITH FINITE ELEMENT NETWORKS |
d253264964 | Enabling private inference is crucial for many cloud inference services that are based on Transformer models. However, existing private inference solutions can increase the inference latency by more than 60× or significantly compromise the inference quality. In this paper, we design the framework MPCFORMER as a practic... | MPCFORMER: FAST, PERFORMANT AND PRIVATE TRANSFORMER INFERENCE WITH MPC |
d21731691 | Explicit encoding of group actions in deep features makes it possible for convolutional neural networks (CNNs) to handle global deformations of images, which is critical to success in many vision tasks. This paper proposes to decompose the convolutional filters over joint steerable bases across the space and the group ... | RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks |
d254823652 | The robustness of machine learning algorithms to distributions shift is primarily discussed in the context of supervised learning (SL). As such, there is a lack of insight on the robustness of the representations learned from unsupervised methods, such as self-supervised learning (SSL) and auto-encoder based algorithms... | HOW ROBUST IS UNSUPERVISED REPRESENTATION LEARNING TO DISTRIBUTION SHIFT? |
d221112371 | To get Bayesian neural networks to perform comparably to standard neural networks it is usually necessary to artificially reduce uncertainty using a "tempered" or "cold" posterior. This is extremely concerning: if the prior is accurate, Bayes inference/decision theory is optimal, and any artificial changes to the poste... | A statistical theory of cold posteriors in deep neural networks |
d256416103 | Recent state-of-the-art source-free domain adaptation (SFDA) methods have focused on learning meaningful cluster structures in the feature space, which have succeeded in adapting the knowledge from source domain to unlabeled target domain without accessing the private source data. However, existing methods rely on the ... | WHEN SOURCE-FREE DOMAIN ADAPTATION MEETS LEARNING WITH NOISY LABELS |
d246822636 | One critical component in lossy deep image compression is the entropy model, which predicts the probability distribution of the quantized latent representation in the encoding and decoding modules. Previous works build entropy models upon convolutional neural networks which are inefficient in capturing global dependenc... | ENTROFORMER: A TRANSFORMER-BASED ENTROPY MODEL FOR LEARNED IMAGE COMPRESSION |
d246652106 | Face clustering has attracted rising research interest recently to take advantage of massive amounts of face images on the web. State-of-the-art performance has been achieved by Graph Convolutional Networks (GCN) due to their powerful representation capacity. However, existing GCN-based methods build face graphs mainly... | ADA-NETS: FACE CLUSTERING VIA ADAPTIVE NEIGHBOUR DISCOVERY IN THE STRUCTURE SPACE |
d252596252 | Diffusion models have been recently studied as powerful generative inverse problem solvers, owing to their high quality reconstructions and the ease of combining existing iterative solvers. However, most works focus on solving simple linear inverse problems in noiseless settings, which significantly under-represents th... | DIFFUSION POSTERIOR SAMPLING FOR GENERAL NOISY INVERSE PROBLEMS |
d198986015 | Generative models are emerging as promising tools in robotics and reinforcement learning. Yet, even though tasks in these domains typically involve distinct objects, most state-of-the-art methods do not explicitly capture the compositional nature of visual scenes. Two exceptions, MONet and IODINE, decompose scenes into... | GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations |
d235436157 | Domain adaptation is an important problem and often needed for real-world applications. In this problem, instead of i.i.d. training and testing datapoints, we assume that the source (training) data and the target (testing) data have different distributions. With that setting, the empirical risk minimization training pr... | KL GUIDED DOMAIN ADAPTATION |
d258833272 | The online knapsack problem is a classic problem in the field of online algorithms. Its canonical version asks how to pack items of different values and weights arriving online into a capacity-limited knapsack so as to maximize the total value of the admitted items. Although optimal competitive algorithms are known for... | Time Fairness in Online Knapsack Problems |
d239049848 | Reinforcement learning (RL) experiments have notoriously high variance, and minor details can have disproportionately large effects on measured outcomes.This is problematic for creating reproducible research and also serves as an obstacle when applying RL to sensitive real-world applications.In this paper, we investiga... | IS HIGH VARIANCE UNAVOIDABLE IN RL? A CASE STUDY IN CONTINUOUS CONTROL |
d263831010 | Recently, pre-trained foundation models have enabled significant advancements in multiple fields.In molecular machine learning, however, where datasets are often hand-curated, and hence typically small, the lack of datasets with labeled features, and codebases to manage those datasets, has hindered the development of f... | TOWARDS FOUNDATIONAL MODELS FOR MOLECULAR LEARNING ON LARGE-SCALE MULTI-TASK DATASETS |
d3609219 | We present Adaptive Memory Networks (AMN) that processes input-question pairs to dynamically construct a network architecture optimized for lower inference times for Question Answering (QA) tasks. AMN processes the input story to extract entities and stores them in memory banks. Starting from a single bank, as the numb... | Adaptive Memory Networks |
d238407831 | Climate change is a major threat to humanity, and the actions required to prevent its catastrophic consequences include changes in both policy-making and individual behaviour. However, taking action requires understanding the effects of climate change, even though they may seem abstract and distant. Projecting the pote... | ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods |
d259095535 | AlphaZero-type algorithms may stop improving on single-player tasks in case the value network guiding the tree search is unable to approximate the outcome of an episode sufficiently well. One technique to address this problem is transforming the single-player task through self-competition. The main idea is to compute a... | POLICY-BASED SELF-COMPETITION FOR PLANNING PROBLEMS |
d253224188 | One-shot coreset selection aims to select a representative subset of the training data, given a pruning rate, that can later be used to train future models while retaining high accuracy. State-of-the-art coreset selection methods pick the highest importance examples based on an importance metric and are found to perfor... | COVERAGE-CENTRIC CORESET SELECTION FOR HIGH PRUNING RATES |
d258298441 | Feature distillation makes the student mimic the intermediate features of the teacher. Nearly all existing feature-distillation methods use L2 distance or its slight variants as the distance metric between teacher and student features. However, while L2 distance is isotropic w.r.t. all dimensions, the neural network's ... | FUNCTION-CONSISTENT FEATURE DISTILLATION |
d231924830 | Spatial memory, or the ability to remember and recall specific locations and objects, is central to autonomous agents' ability to carry out tasks in real environments. However, most existing artificial memory modules are not very adept at storing spatial information. We propose a parameter-free module, Egospheric Spati... | END-TO-END EGOSPHERIC SPATIAL MEMORY |
d252595765 | Feature selection is the problem of selecting a subset of features for a machine learning model that maximizes model quality subject to a budget constraint. For neural networks, prior methods, including those based on ℓ 1 regularization, attention, and other techniques, typically select the entire feature subset in one... | SEQUENTIAL ATTENTION FOR FEATURE SELECTION |
d58006571 | The adversarial training procedure proposed byMadry et al. (2018)is one of the most effective methods to defend against adversarial examples in deep neural networks (DNNs). In our paper, we shed some lights on the practicality and the hardness of adversarial training by showing that the effectiveness (robustness on tes... | THE LIMITATIONS OF ADVERSARIAL TRAINING AND THE BLIND-SPOT ATTACK |
d3463636 | Recent studies show that widely used deep neural networks (DNNs) are vulnerable to carefully crafted adversarial examples. Many advanced algorithms have been proposed to generate adversarial examples by leveraging the L p distance for penalizing perturbations. Researchers have explored different defense methods to defe... | SPATIALLY TRANSFORMED ADVERSARIAL EXAMPLES |
d254198961 | Poor sample efficiency continues to be the primary challenge for deployment of deep Reinforcement Learning (RL) algorithms for real-world applications, and in particular for visuo-motor control. Model-based RL has the potential to be highly sample efficient by concurrently learning a world model and using synthetic rol... | MODEM: ACCELERATING VISUAL MODEL-BASED REINFORCEMENT LEARNING WITH DEMONSTRATIONS |
d249209856 | SHAP explanations aim at identifying which features contribute the most to the difference in model prediction at a specific input versus a background distribution. Recent studies have shown that they can be manipulated by malicious adversaries to produce arbitrary desired explanations. However, existing attacks focus s... | Fooling SHAP with Stealthily Biased Sampling |
d67855499 | While Bayesian neural networks (BNNs) have drawn increasing attention, their posterior inference remains challenging, due to the high-dimensional and overparameterized nature. Recently, several highly flexible and scalable variational inference procedures based on the idea of particle optimization have been proposed. T... | FUNCTION SPACE PARTICLE OPTIMIZATION FOR BAYESIAN NEURAL NETWORKS |
d258967589 | Preference-based Reinforcement Learning (PbRL) is a paradigm in which an RL agent learns to optimize a task using pair-wise preference-based feedback over trajectories, rather than explicit reward signals. While PbRL has demonstrated practical success in fine-tuning language models, existing theoretical work focuses on... | Provable Reward-Agnostic Preference-Based Reinforcement Learning |
d257219472 | With adversarial or otherwise normal prompts, existing large language models (LLM) can be pushed to generate toxic discourses. One way to reduce the risk of LLMs generating undesired discourses is to alter the training of the LLM. This can be very restrictive due to demanding computation requirements. Other methods rel... | SYSTEMATIC RECTIFICATION OF LANGUAGE MODELS VIA DEAD-END ANALYSIS |
d258180544 | Contemporary applications of machine learning in two-team e-sports and the superior expressivity of multi-agent generative adversarial networks raise important and overlooked theoretical questions regarding optimization in two-team games. Formally, two-team zero-sum games are defined as multi-player games where players... | Towards convergence to Nash equilibria in two-team zero-sum games |
d210473083 | Most generative models of audio directly generate samples in one of two domains: time or frequency. While sufficient to express any signal, these representations are inefficient, as they do not utilize existing knowledge of how sound is generated and perceived. A third approach (vocoders/synthesizers) successfully inco... | DDSP: DIFFERENTIABLE DIGITAL SIGNAL PROCESSING |
d51559 | Recurrent neural networks are a powerful tool for modeling sequential data, but the dependence of each timestep's computation on the previous timestep's output limits parallelism and makes RNNs unwieldy for very long sequences. We introduce quasi-recurrent neural networks (QRNNs), an approach to neural sequence modelin... | QUASI-RECURRENT NEURAL NETWORKS |
d252734944 | Equivariance guarantees that a model's predictions capture key symmetries in data. When an image is translated or rotated, an equivariant model's representation of that image will translate or rotate accordingly. The success of convolutional neural networks has historically been tied to translation equivariance directl... | The Lie Derivative for Measuring Learned Equivariance |
d245668925 | Text adventure games present unique challenges to reinforcement learning methods due to their combinatorially large action spaces and sparse rewards. The interplay of these two factors is particularly demanding because large action spaces require extensive exploration, while sparse rewards provide limited feedback. Thi... | MULTI-STAGE EPISODIC CONTROL FOR STRATEGIC EXPLORATION IN TEXT GAMES |
d222378250 | Self-supervised learning has emerged as a strategy to reduce the reliance on costly supervised signal by pretraining representations only using unlabeled data. These methods combine heuristic proxy classification tasks with data augmentations and have achieved significant success, but our theoretical understanding of t... | REPRESENTATION LEARNING VIA INVARIANT CAUSAL MECHANISMS |
d108308108 | A key challenge in reinforcement learning (RL) is environment generalization: a policy trained to solve a task in one environment often fails to solve the same task in a slightly different test environment. A common approach to improve interenvironment transfer is to learn policies that are invariant to the distributio... | ENVIRONMENT PROBING INTERACTION POLICIES |
d258564352 | The exploration problem is one of the main challenges in deep reinforcement learning (RL). Recent promising works tried to handle the problem with populationbased methods, which collect samples with diverse behaviors derived from a population of different exploratory policies. Adaptive policy selection has been adopted... | LEARNABLE BEHAVIOR CONTROL: BREAKING ATARI HUMAN WORLD RECORDS VIA SAMPLE-EFFICIENT BEHAVIOR SELECTION |
d251066616 | The problem of end-to-end learning of a communication system using an autoencoder -consisting of an encoder, channel, and decoder modeled using neural networks -has recently been shown to be an effective approach. A challenge faced in the practical adoption of this learning approach is that under changing channel condi... | FEW-SHOT DOMAIN ADAPTATION FOR END-TO-END COMMUNICATION |
d252993068 | While Mixed-integer linear programming (MILP) is NP-hard in general, practical MILP has received roughly 100-fold speedup in the past twenty years. Still, many classes of MILPs quickly become unsolvable as their sizes increase, motivating researchers to seek new acceleration techniques for MILPs. With deep learning, th... | ON REPRESENTING MIXED-INTEGER LINEAR PROGRAMS BY GRAPH NEURAL NETWORKS |
d238419564 | For many differentially private algorithms, such as the prominent noisy stochastic gradient descent (DP-SGD), the analysis needed to bound the privacy leakage of a single training run is well understood. However, few studies have reasoned about the privacy leakage resulting from the multiple training runs needed to fin... | HYPERPARAMETER TUNING WITH RENYI DIFFERENTIAL PRIVACY |
d235613557 | Understanding human behavior from observed data is critical for transparency and accountability in decision-making.Consider real-world settings such as healthcare, in which modeling a decision-maker's policy is challenging-with no access to underlying states, no knowledge of environment dynamics, and no allowance for l... | EXPLAINING BY IMITATING: UNDERSTANDING DECISIONS BY INTERPRETABLE POLICY LEARNING |
d258947197 | This paper investigates a missing feature imputation problem for graph learning tasks. Several methods have previously addressed learning tasks on graphs with missing features. However, in cases of high rates of missing features, they were unable to avoid significant performance degradation. To overcome this limitation... | CONFIDENCE-BASED FEATURE IMPUTATION FOR GRAPHS WITH PARTIALLY KNOWN FEATURES |
d239050418 | Robustness to distribution shifts is critical for deploying machine learning models in the real world. Despite this necessity, there has been little work in defining the underlying mechanisms that cause these shifts and evaluating the robustness of algorithms across multiple, different distribution shifts. To this end,... | A FINE-GRAINED ANALYSIS ON DISTRIBUTION SHIFT |
d263829511 | Figure 1: Comparison between our method, Linear Autoregressive Similarity Index (LASI), and Learned Perceptual Image Patch Similarity (LPIPS) from Zhang et al. (2018).Our method solves a weighted least squares (WLS) problem to compute perceptual embeddings, at inference time, with no prior training or neural networks.L... | THE UNREASONABLE EFFECTIVENESS OF LINEAR PREDICTION AS A PERCEPTUAL METRIC |
d211082795 | Local robustness ensures that a model classifies all inputs within an -ball consistently, which precludes various forms of adversarial inputs. In this paper, we present a fast procedure for checking local robustness in feed-forward neural networks with piecewise linear activation functions. The key insight is that such... | Fast Geometric Projections for Local Robustness Certification |
d233306870 | Machine learning systems typically assume that the distributions of training and test sets match closely. However, a critical requirement of such systems in the real world is their ability to generalize to unseen domains. Here, we propose an inter-domain gradient matching objective that targets domain generalization by... | Gradient Matching for Domain Generalization |
d252683303 | We study the task of prompting large-scale language models to perform multistep reasoning. Existing work shows that when prompted with a chain of thoughts (CoT), sequences of short sentences describing intermediate reasoning steps towards a final answer, large language models can generate new reasoning chains and predi... | COMPLEXITY-BASED PROMPTING FOR MULTI-STEP REASONING |
d2483823 | Deep Convolutional Neuronal Networks (DCNNs) are showing remarkable performance on many computer vision tasks. Due to their large parameter space, they require many labeled samples when trained in a supervised setting. The costs of annotating data manually can render the use of DCNNs infeasible. We present a novel fram... | RenderGAN: Generating Realistic Labeled Data |
d248965297 | Human language is grounded on multimodal knowledge including visual knowledge like colors, sizes, and shapes. However, current large-scale pre-trained language models rely on text-only self-supervised training with massive text data, which precludes them from utilizing relevant visual information when necessary. To add... | VISUALLY-AUGMENTED LANGUAGE MODELING |
d256868350 | Today, ground-truth generation uses data sets annotated by cloud-based annotation services. These services rely on human annotation, which can be prohibitively expensive. In this paper, we consider the problem of hybrid human-machine labeling, which trains a classifier to accurately auto-label part of the data set. How... | MCAL: MINIMUM COST HUMAN-MACHINE ACTIVE LABELING |
d255440465 | Skip-Attention: Improving Vision Transformers by Paying Less Attention | |
d244729050 | We present Path Integral Sampler (PIS), a novel algorithm to draw samples from unnormalized probability density functions. The PIS is built on the Schrödinger bridge problem which aims to recover the most likely evolution of a diffusion process given its initial distribution and terminal distribution. The PIS draws sam... | PATH INTEGRAL SAMPLER: A STOCHASTIC CONTROL APPROACH FOR SAMPLING |
d226221794 | Recently, invariant risk minimization (IRM) was proposed as a promising solution to address out-of-distribution (OOD) generalization. However, it is unclear when IRM should be preferred over the widely-employed empirical risk minimization (ERM) framework. In this work, we analyze both these frameworks from the perspect... | Empirical or Invariant Risk Minimization? A Sample Complexity Perspective |
d259298560 | Representational straightening refers to a decrease in curvature of visual feature representations of a sequence of frames taken from natural movies. Prior work established straightening in neural representations of the primate primary visual cortex (V1) and perceptual straightening in human behavior as a hallmark of b... | BRAIN-LIKE REPRESENTATIONAL STRAIGHTENING OF NATURAL MOVIES IN ROBUST FEEDFORWARD NEURAL NETWORKS |
d238583478 | Graph neural networks achieve high accuracy in link prediction by jointly leveraging graph topology and node attributes. Topology, however, is represented indirectly; state-of-the-art methods based on subgraph classification label nodes with distance to the target link, so that, although topological information is pres... | Neural Link Prediction with Walk Pooling |
d67856138 | We consider the dictionary learning problem, where the aim is to model the given data as a linear combination of a few columns of a matrix known as a dictionary, where the sparse weights forming the linear combination are known as coefficients. Since the dictionary and coefficients, parameterizing the linear model are ... | NOODL: PROVABLE ONLINE DICTIONARY LEARNING AND SPARSE CODING |
d258048507 | Meta-learning has arisen as a successful method for improving training performance by training over many similar tasks, especially with deep neural networks (DNNs). However, the theoretical understanding of when and why overparameterized models such as DNNs can generalize well in meta-learning is still limited. As an i... | Theoretical Characterization of the Generalization Performance of Overfitted Meta-Learning |
d263830943 | This work considers a rather general and broad class of Markov chains, Ito chains that look like Euler-Maryama discretization of some Stochastic Differential Equation.The chain we study is a unified framework for theoretical analysis.It comes with almost arbitrary isotropic and state-dependent noise instead of normal a... | Ito Diffusion Approximation of Universal Ito Chains for Sampling, Optimization and Boosting |
d23873820 | We explore efficient neural architecture search methods and present a simple yet powerful evolutionary algorithm that can discover new architectures achieving state of the art results. Our approach combines a novel hierarchical genetic representation scheme that imitates the modularized design pattern commonly adopted ... | HIERARCHICAL REPRESENTATIONS FOR EFFICIENT ARCHITECTURE SEARCH |
d235614244 | As an essential ingredient of modern deep learning, attention mechanism, especially self-attention, plays a vital role in the global correlation discovery. However, is hand-crafted attention irreplaceable when modeling the global context? Our intriguing finding is that self-attention is not better than the matrix decom... | IS ATTENTION BETTER THAN MATRIX DECOMPOSITION? |
d222132819 | Role-based learning holds the promise of achieving scalable multi-agent learning by decomposing complex tasks using roles. However, it is largely unclear how to efficiently discover such a set of roles. To solve this problem, we propose to first decompose joint action spaces into restricted role action spaces by cluste... | RODE: LEARNING ROLES TO DECOMPOSE MULTI-AGENT TASKS |
d222177039 | In this paper, we revisit variational intrinsic control (VIC), an unsupervised reinforcement learning method for finding the largest set of intrinsic options available to an agent. In the original work byGregor et al. (2016), two VIC algorithms were proposed: one that represents the options explicitly, and the other th... | VARIATIONAL INTRINSIC CONTROL REVISITED |
d247939726 | The goal of dynamic scene deblurring is to remove the motion blur in a given image. Typical learning-based approaches implement their solutions by minimizing the L1 or L2 distance between the output and the reference sharp image. Recent attempts adopt visual recognition features in training to improve the perceptual qu... | CLEAN IMAGES ARE HARD TO REBLUR: EXPLOITING THE ILL-POSED INVERSE TASK FOR DYNAMIC SCENE DEBLURRING |
d264825424 | Text-to-3D generation has made remarkable progress recently, particularly with methods based on Score Distillation Sampling (SDS) that leverages pre-trained 2D diffusion models.While the usage of classifier-free guidance is well acknowledged to be crucial for successful optimization, it is considered an auxiliary trick... | TEXT-TO-3D WITH CLASSIFIER SCORE DISTILLATION |
d263791008 | In Causal Bayesian Optimization (CBO), an agent intervenes on an unknown structural causal model to maximize a downstream reward variable. In this paper, we consider the generalization where other agents or external events also intervene on the system, which is key for enabling adaptiveness to non-stationarities such a... | ADVERSARIAL CAUSAL BAYESIAN OPTIMIZATION |
d253080708 | Graph neural networks (GNNs) are able to leverage the structure of graph data by passing messages along the edges of the graph. While this allows GNNs to learn features depending on the graph structure, for certain graph topologies it leads to inefficient information propagation and a problem known as oversquashing. Th... | FOSR: FIRST-ORDER SPECTRAL REWIRING FOR ADDRESSING OVERSQUASHING IN GNNS |
d53113561 | Conversational machine comprehension requires a deep understanding of the conversation history. To enable traditional, single-turn models to encode the history comprehensively, we introduce FLOW, a mechanism that can incorporate intermediate representations generated during the process of answering previous questions, ... | FLOWQA: GRASPING FLOW IN HISTORY FOR CONVERSATIONAL MACHINE COMPREHENSION |
d147704157 | The Adam algorithm has become extremely popular for large-scale machine learning. Under convexity condition, it has been proved to enjoy a data-dependant O( √ T ) regret bound where T is the time horizon. However, whether strong convexity can be utilized to further improve the performance remains an open problem. In th... | SAdam: A Variant of Adam for Strongly Convex Functions |
d229923128 | Knowledge distillation (KD) is essential for training non-autoregressive translation (NAT) models by reducing the complexity of the raw data with an autoregressive teacher model. In this study, we empirically show that as a side effect of this training, the lexical choice errors on low-frequency words are propagated to... | UNDERSTANDING AND IMPROVING LEXICAL CHOICE IN NON-AUTOREGRESSIVE TRANSLATION |
d238856778 | Few-shot class incremental learning-the problem of updating a trained classifier to discriminate among an expanded set of classes with limited labeled data-is a key challenge for machine learning systems deployed in non-stationary environments. Existing approaches to the problem rely on complex model architectures and ... | SUBSPACE REGULARIZERS FOR FEW-SHOT CLASS INCREMENTAL LEARNING |
d235489741 | We advocate for a practical Maximum Likelihood Estimation (MLE) approach towards designing loss functions for regression and forecasting, as an alternative to the typical approach of direct empirical risk minimization on a specific target metric. The MLE approach is better suited to capture inductive biases such as pri... | On the benefits of maximum likelihood estimation for Regression and Forecasting |
d263672002 | Given a set of K probability densities, we consider the multimarginal generative modeling problem of learning a joint distribution that recovers these densities as marginals.The structure of this joint distribution should identify multi-way correspondences among the prescribed marginals.We formalize an approach to this... | MULTIMARGINAL GENERATIVE MODELING WITH STOCHASTIC INTERPOLANTS |
d260351224 | Assigning importance weights to adversarial data has achieved great success in training adversarially robust networks under limited model capacity. However, existing instance-reweighted adversarial training (AT) methods heavily depend on heuristics and/or geometric interpretations to determine those importance weights,... | Doubly Robust Instance-Reweighted Adversarial Training |
d108297416 | This paper describes a new form of unsupervised learning, whose input is a set of unlabeled points that are assumed to be local maxima of an unknown value function v in an unknown subset of the vector space. Two functions are learned: (i) a set indicator c, which is a binary classifier, and (ii) a comparator function h... | UNSUPERVISED LEARNING OF THE SET OF LOCAL MAXIMA |
d256663850 | More and more evidence has shown that strengthening layer interactions can enhance the representation power of a deep neural network, while self-attention excels at learning interdependencies by retrieving query-activated information. Motivated by this, we devise a cross-layer attention mechanism, called multi-head rec... | CROSS-LAYER RETROSPECTIVE RETRIEVING VIA LAYER ATTENTION |
d263829757 | We introduce JointNet, a novel neural network architecture for modeling the joint distribution of images and an additional dense modality (e.g., depth maps).Joint-Net is extended from a pre-trained text-to-image diffusion model, where a copy of the original network is created for the new dense modality branch and is de... | JOINTNET: EXTENDING TEXT-TO-IMAGE DIFFUSION FOR DENSE DISTRIBUTION MODELING |
d263136146 | Recent advancements in autonomous driving have relied on data-driven approaches, which are widely adopted but face challenges including dataset bias, overfitting, and uninterpretability.Drawing inspiration from the knowledge-driven nature of human driving, we explore the question of how to instill similar capabilities ... | DILU: A KNOWLEDGE-DRIVEN APPROACH TO AUTONOMOUS DRIVING WITH LARGE LANGUAGE MODELS |
d212414027 | We propose an energy-based model (EBM) of protein conformations that operates at atomic scale. The model is trained solely on crystallized protein data. By contrast, existing approaches for scoring conformations use energy functions that incorporate knowledge of physical principles and features that are the complex pro... | ENERGY-BASED MODELS FOR ATOMIC-RESOLUTION PROTEIN CONFORMATIONS |
d259203325 | It is often advantageous to train models on a subset of the available train examples, because the examples are of variable quality or because one would like to train with fewer examples, without sacrificing performance. We present Gradient Information Optimization (GIO), a scalable, task-agnostic approach to this data ... | GIO: GRADIENT INFORMATION OPTIMIZATION FOR TRAINING DATASET SELECTION |
d222140947 | Contrastive learning has been adopted as a core method for unsupervised visual representation learning. Without human annotation, the common practice is to perform an instance discrimination task: Given a query image crop, this task labels crops from the same image as positives, and crops from other randomly sampled im... | CO2: CONSISTENT CONTRAST FOR UNSUPERVISED VISUAL REPRESENTATION LEARNING |
d235485093 | Recent work in equivariant deep learning bears strong similarities to physics. Fields over a base space are fundamental entities in both subjects, as are equivariant maps between these fields. In deep learning, however, these maps are usually defined by convolutions with a kernel, whereas they are partial differential ... | STEERABLE PARTIAL DIFFERENTIAL OPERATORS FOR EQUIVARIANT NEURAL NETWORKS |
d264452015 | We present DreamCraft3D, a hierarchical 3D content generation method that produces high-fidelity and coherent 3D objects.We tackle the problem by leveraging a 2D reference image to guide the stages of geometry sculpting and texture boosting.A central focus of this work is to address the consistency issue that existing ... | DREAMCRAFT3D: HIERARCHICAL 3D GENERATION WITH BOOTSTRAPPED DIFFUSION PRIOR |
d259138847 | This paper studies the curious phenomenon for machine learning models with Transformer architectures that their activation maps are sparse. By activation map we refer to the intermediate output of the multi-layer perceptrons (MLPs) after a ReLU activation function, and by "sparse" we mean that on average very few entri... | The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers |
d236087421 | An Axial Shifted MLP architecture (AS-MLP) is proposed in this paper. Different from MLP-Mixer, where the global spatial feature is encoded for information flow through matrix transposition and one token-mixing MLP, we pay more attention to the local features interaction. By axially shifting channels of the feature map... | AS-MLP: AN AXIAL SHIFTED MLP ARCHITECTURE FOR VISION |
d240070408 | Deep neural networks (DNNs) are proved to be vulnerable against backdoor attacks. A backdoor is often embedded in the target DNNs through injecting a backdoor trigger into training examples, which can cause the target DNNs misclassify an input attached with the backdoor trigger. Existing backdoor detection methods ofte... | AEVA: BLACK-BOX BACKDOOR DETECTION USING ADVERSARIAL EXTREME VALUE ANALYSIS |
d256662465 | Incorporating equivariance to symmetry groups as a constraint during neural network training can improve performance and generalization for tasks exhibiting those symmetries, but such symmetries are often not perfectly nor explicitly present. This motivates algorithmically optimizing the architectural constraints impos... | EQUIVARIANCE-AWARE ARCHITECTURAL OPTIMIZATION OF NEURAL NETWORKS |
d246276208 | The ability to learn reward functions plays an important role in enabling the deployment of intelligent agents in the real world. However, comparing reward functions, for example as a means of evaluating reward learning methods, presents a challenge. Reward functions are typically compared by considering the behavior o... | DYNAMICS-AWARE COMPARISON OF LEARNED REWARD FUNCTIONS |
d250048824 | Linear time-invariant state space models (SSM) are a classical model from engineering and statistics, that have recently been shown to be very promising in machine learning through the Structured State Space sequence model (S4). A core component of S4 involves initializing the SSM state matrix to a particular matrix ca... | How to Train Your HiPPO: State Space Models with Generalized Orthogonal Basis Projections |
d225103395 | A key factor in the success of deep neural networks is the ability to scale models to improve performance by varying the architecture depth and width. This simple property of neural network design has resulted in highly effective architectures for a variety of tasks. Nevertheless, there is limited understanding of effe... | DO WIDE AND DEEP NETWORKS LEARN THE SAME THINGS? UNCOVERING HOW NEURAL NETWORK REPRESENTATIONS VARY WITH WIDTH AND DEPTH |
d20827927 | Disentangled representations, where the higher level data generative factors are reflected in disjoint latent dimensions, offer several benefits such as ease of deriving invariant representations, transferability to other tasks, interpretability, etc. We consider the problem of unsupervised learning of disentangled rep... | Variational Inference of Disentangled Latent Concepts from Unlabeled Observations |
d254044657 | Algorithmic fairness plays an increasingly critical role in machine learning research.Several group fairness notions and algorithms have been proposed.However, the fairness guarantee of existing fair classification methods mainly depends on specific data distributional assumptions, often requiring large sample sizes, a... | FaiREE: Fair Classification with Finite-Sample and Distribution-Free Guarantee |
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