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3D Self-Supervised Methods for Medical Imaging

3D Shape Reconstruction from Vision and Touch

A Benchmark for Systematic Generalization in Grounded Language Understanding

A biologically plausible neural network for Slow Feature Analysis

A Boolean Task Algebra for Reinforcement Learning

A causal view of compositional zero-shot recognition

Accelerating Reinforcement Learning through GPU Atari Emulation

Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping

Achieving Equalized Odds by Resampling Sensitive Attributes

A Class of Algorithms for General Instrumental Variable Models

A Closer Look at Accuracy vs. Robustness

A Closer Look at the Training Strategy for Modern Meta-Learning

A Combinatorial Perspective on Transfer Learning

A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval

A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions

A Convolutional Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction

Active Invariant Causal Prediction: Experiment Selection through Stability

Active Structure Learning of Causal DAGs via Directed Clique Trees

AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients

Adapting Neural Architectures Between Domains

3D Self-Supervised Methods for Medical Imaging

3D Shape Reconstruction from Vision and Touch

A Benchmark for Systematic Generalization in Grounded Language Understanding

A biologically plausible neural network for Slow Feature Analysis

A Boolean Task Algebra for Reinforcement Learning

A causal view of compositional zero-shot recognition

Accelerating Reinforcement Learning through GPU Atari Emulation

Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping

Achieving Equalized Odds by Resampling Sensitive Attributes

A Class of Algorithms for General Instrumental Variable Models

A Closer Look at Accuracy vs. Robustness

A Closer Look at the Training Strategy for Modern Meta-Learning

A Combinatorial Perspective on Transfer Learning

A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval

A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions

A Convolutional Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction

Active Invariant Causal Prediction: Experiment Selection through Stability

Active Structure Learning of Causal DAGs via Directed Clique Trees

AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients

Adapting Neural Architectures Between Domains

Adaptive Discretization for Model-Based Reinforcement Learning

Adaptive Gradient Quantization for Data-Parallel SGD

Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting

Adaptive Learning of Rank-One Models for Efficient Pairwise Sequence Alignment

Adaptive Reduced Rank Regression

Adaptive Sampling for Stochastic Risk-Averse Learning

AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning

A Discrete Variational Recurrent Topic Model without the Reparametrization Trick

Adversarial Counterfactual Learning and Evaluation for Recommender System

Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion

Adversarial Distributional Training for Robust Deep Learning

Adversarial Example Games

Adversarial Learning for Robust Deep Clustering

Adversarially Robust Few-Shot Learning: A Meta-Learning Approach

Adversarial Robustness of Supervised Sparse Coding

Adversarial Self-Supervised Contrastive Learning

Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization

Adversarial Sparse Transformer for Time Series Forecasting

Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation

Adversarial Weight Perturbation Helps Robust Generalization

AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows

A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding

A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning

Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient Space

A Group-Theoretic Framework for Data Augmentation

AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity

Algorithmic recourse under imperfect causal knowledge: a probabilistic approach

All Word Embeddings from One Embedding

All your loss are belong to Bayes

Almost Surely Stable Deep Dynamics

A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model

An Analysis of SVD for Deep Rotation Estimation

An analytic theory of shallow networks dynamics for hinge loss classification

An Efficient Adversarial Attack for Tree Ensembles

An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy Search

An Efficient Framework for Clustered Federated Learning

An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay

A new inference approach for training shallow and deep generalized linear models of noisy interacting neurons

An Improved Analysis of Stochastic Gradient Descent with Momentum

A polynomial-time algorithm for learning nonparametric causal graphs

Approximate Cross-Validation for Structured Models

Approximation Based Variance Reduction for Reparameterization Gradients

A Randomized Algorithm to Reduce the Support of Discrete Measures

A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection

ARMA Nets: Expanding Receptive Field for Dense Prediction

A Simple Language Model for Task-Oriented Dialogue

A Spectral Energy Distance for Parallel Speech Synthesis

A Statistical Framework for Low-bitwidth Training of Deep Neural Networks

A Study on Encodings for Neural Architecture Search

Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability

Asymptotically Optimal Exact Minibatch Metropolis-Hastings

A Theoretical Framework for Target Propagation

A Topological Filter for Learning with Label Noise

Attack of the Tails: Yes, You Really Can Backdoor Federated Learning

Attention-Gated Brain Propagation: How the brain can implement reward-based error backpropagation

Attribution Preservation in Network Compression for Reliable Network Interpretation

Audeo: Audio Generation for a Silent Performance Video

Auditing Differentially Private Machine Learning: How Private is Private SGD?

Autoencoders that don't overfit towards the Identity

Autofocused oracles for model-based design

Auto Learning Attention

Automatically Learning Compact Quality-aware Surrogates for Optimization Problems

Automatic Curriculum Learning through Value Disagreement

Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond

Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation

A Variational Approach for Learning from Positive and Unlabeled Data

AvE: Assistance via Empowerment

AViD Dataset: Anonymized Videos from Diverse Countries

Avoiding Side Effects in Complex Environments

Backpropagating Linearly Improves Transferability of Adversarial Examples

Bad Global Minima Exist and SGD Can Reach Them

BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning

Balanced Meta-Softmax for Long-Tailed Visual Recognition

BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits

Bandit Samplers for Training Graph Neural Networks

Barking up the right tree: an approach to search over molecule synthesis DAGs

Baxter Permutation Process

Bayesian Attention Modules

Bayesian Deep Ensembles via the Neural Tangent Kernel

Bayesian Deep Learning and a Probabilistic Perspective of Generalization

Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods

Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels

Bayesian Optimization for Iterative Learning

Bayesian Optimization of Risk Measures

Bayesian Probabilistic Numerical Integration with Tree-Based Models

Bayesian Pseudocoresets

Bayesian Robust Optimization for Imitation Learning

BayReL: Bayesian Relational Learning for Multi-omics Data Integration

Benchmarking deep inverse models over time, and the neural-adjoint method

Benchmarking Deep Learning Interpretability in Time Series Predictions

BERT Loses Patience: Fast and Robust Inference with Early Exit

Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs

Better Set Representations For Relational Reasoning

Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency

Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs

Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties

Bidirectional Convolutional Poisson Gamma Dynamical Systems

Big Bird: Transformers for Longer Sequences

Big Self-Supervised Models are Strong Semi-Supervised Learners

Bi-level Score Matching for Learning Energy-based Latent Variable Models

Biologically Inspired Mechanisms for Adversarial Robustness

Black-Box Optimization with Local Generative Surrogates

Black-Box Ripper: Copying black-box models using generative evolutionary algorithms

Blind Video Temporal Consistency via Deep Video Prior

BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images

Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning

Boosting Adversarial Training with Hypersphere Embedding

Boosting First-Order Methods by Shifting Objective: New Schemes with Faster Worst-Case Rates

Bootstrapping Neural Processes

Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning

BOSS: Bayesian Optimization over String Spaces

BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization

Boundary thickness and robustness in learning models

BoxE: A Box Embedding Model for Knowledge Base Completion

Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning

Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS

BRP-NAS: Prediction-based NAS using GCNs

Building powerful and equivariant graph neural networks with structural message-passing

Byzantine Resilient Distributed Multi-Task Learning

Calibrating Deep Neural Networks using Focal Loss

Can Graph Neural Networks Count Substructures?

Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction

Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?

Cascaded Text Generation with Markov Transformers

CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations

CASTLE: Regularization via Auxiliary Causal Graph Discovery

Causal Discovery in Physical Systems from Videos

Causal Intervention for Weakly-Supervised Semantic Segmentation

Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models

Certifiably Adversarially Robust Detection of Out-of-Distribution Data

Certified Defense to Image Transformations via Randomized Smoothing

Certified Monotonic Neural Networks

Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks

CHIP: A Hawkes Process Model for Continuous-time Networks with Scalable and Consistent Estimation

CircleGAN: Generative Adversarial Learning across Spherical Circles

Classification with Valid and Adaptive Coverage

CLEARER: Multi-Scale Neural Architecture Search for Image Restoration

Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow

CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection

CogLTX: Applying BERT to Long Texts

Coherent Hierarchical Multi-Label Classification Networks

CoinPress: Practical Private Mean and Covariance Estimation

Collapsing Bandits and Their Application to Public Health Intervention

Color Visual Illusions: A Statistics-based Computational Model

Combining Deep Reinforcement Learning and Search for Imperfect-Information Games

CoMIR: Contrastive Multimodal Image Representation for Registration

Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian

Community detection using fast low-cardinality semidefinite programming

Compositional Explanations of Neurons

Compositional Generalization by Learning Analytical Expressions

Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection

Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding

CompRess: Self-Supervised Learning by Compressing Representations

Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programming

Confidence sequences for sampling without replacement

Conformal Symplectic and Relativistic Optimization

Conservative Q-Learning for Offline Reinforcement Learning

Consistency Regularization for Certified Robustness of Smoothed Classifiers

Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations

Consistent Feature Selection for Analytic Deep Neural Networks

Constrained episodic reinforcement learning in concave-convex and knapsack settings

Constraining Variational Inference with Geometric Jensen-Shannon Divergence

Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming

Continual Deep Learning by Functional Regularisation of Memorable Past

Continual Learning in Low-rank Orthogonal Subspaces

Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks

Continuous Meta-Learning without Tasks

Continuous Object Representation Networks: Novel View Synthesis without Target View Supervision

Continuous Regularized Wasserstein Barycenters

Continuous Surface Embeddings

ContraGAN: Contrastive Learning for Conditional Image Generation

Contrastive learning of global and local features for medical image segmentation with limited annotations

ConvBERT: Improving BERT with Span-based Dynamic Convolution

Convergence and Stability of Graph Convolutional Networks on Large Random Graphs

Convex optimization based on global lower second-order models

Convolutional Generation of Textured 3D Meshes

Convolutional Tensor-Train LSTM for Spatio-temporal Learning

CO-Optimal Transport

COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning

Coresets for Regressions with Panel Data

Coresets via Bilevel Optimization for Continual Learning and Streaming

Correlation Robust Influence Maximization

Correspondence Learning via Linearly-invariant Embedding

CoSE: Compositional Stroke Embeddings

COT-GAN: Generating Sequential Data via Causal Optimal Transport

Co-Tuning for Transfer Learning

Counterexample-Guided Learning of Monotonic Neural Networks

Counterfactual Data Augmentation using Locally Factored Dynamics

Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search

Critic Regularized Regression

Cross-lingual Retrieval for Iterative Self-Supervised Training

Cross-Scale Internal Graph Neural Network for Image Super-Resolution

CrossTransformers: spatially-aware few-shot transfer

Cross-validation Confidence Intervals for Test Error

CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances

Curriculum By Smoothing

Curriculum learning for multilevel budgeted combinatorial problems

DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks

Dark Experience for General Continual Learning: a Strong, Simple Baseline

Data Diversification: A Simple Strategy For Neural Machine Translation

Debiased Contrastive Learning

Debugging Tests for Model Explanations

Decision-Making with Auto-Encoding Variational Bayes

Decisions, Counterfactual Explanations and Strategic Behavior

Decision trees as partitioning machines to characterize their generalization properties

Deep active inference agents using Monte-Carlo methods

Deep Archimedean Copulas

Deep Automodulators

Deep Direct Likelihood Knockoffs

Deep Energy-Based Modeling of Discrete-Time Physics

Deep Evidential Regression

Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking

DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs

Deep Imitation Learning for Bimanual Robotic Manipulation

Deep Inverse Q-learning with Constraints

Deeply Learned Spectral Total Variation Decomposition

Deep Multimodal Fusion by Channel Exchanging

Deep reconstruction of strange attractors from time series

Deep Reinforcement and InfoMax Learning

Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games

Deep Shells: Unsupervised Shape Correspondence with Optimal Transport

Deep Statistical Solvers

Deep Structural Causal Models for Tractable Counterfactual Inference

DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation

Deep Transformation-Invariant Clustering

Deep Transformers with Latent Depth

Deep Variational Instance Segmentation

Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring

Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians

Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation

Demixed shared component analysis of neural population data from multiple brain areas

Denoised Smoothing: A Provable Defense for Pretrained Classifiers

Denoising Diffusion Probabilistic Models

Depth Uncertainty in Neural Networks

(De)Randomized Smoothing for Certifiable Defense against Patch Attacks

Design Space for Graph Neural Networks

Dialog without Dialog Data: Learning Visual Dialog Agents from VQA Data

Differentiable Augmentation for Data-Efficient GAN Training

Differentiable Causal Discovery from Interventional Data

Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization

Differentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement

Differentially-Private Federated Linear Bandits

DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling

Digraph Inception Convolutional Networks

Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures

Directional Pruning of Deep Neural Networks

Dirichlet Graph Variational Autoencoder

DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction

Discovering conflicting groups in signed networks

Discovering Reinforcement Learning Algorithms

Discovering Symbolic Models from Deep Learning with Inductive Biases

Discriminative Sounding Objects Localization via Self-supervised Audiovisual Matching

Disentangling by Subspace Diffusion

Disentangling Human Error from Ground Truth in Segmentation of Medical Images

DISK: Learning local features with policy gradient

Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation

Dissecting Neural ODEs

Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning

Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning

Distributionally Robust Federated Averaging

Distributionally Robust Parametric Maximum Likelihood Estimation

Distribution-free binary classification: prediction sets, confidence intervals and calibration

Distribution Matching for Crowd Counting

Diverse Image Captioning with Context-Object Split Latent Spaces

Diversity can be Transferred: Output Diversification for White- and Black-box Attacks

Diversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations

Do Adversarially Robust ImageNet Models Transfer Better?

Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?

Domain Adaptation as a Problem of Inference on Graphical Models

Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift

Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization

Domain Generalization via Entropy Regularization

Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies

Dual-Free Stochastic Decentralized Optimization with Variance Reduction

Dual Instrumental Variable Regression

Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion

Dual-Resolution Correspondence Networks

Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning

DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles

DynaBERT: Dynamic BERT with Adaptive Width and Depth

Dynamic allocation of limited memory resources in reinforcement learning

Early-Learning Regularization Prevents Memorization of Noisy Labels

Effective Diversity in Population Based Reinforcement Learning

Efficient Algorithms for Device Placement of DNN Graph Operators

Efficient Clustering Based On A Unified View Of K -means And Ratio-cut

Efficient Contextual Bandits with Continuous Actions

Efficient Exact Verification of Binarized Neural Networks

Efficient Generation of Structured Objects with Constrained Adversarial Networks

Efficient Learning of Discrete Graphical Models

Efficient Learning of Generative Models via Finite-Difference Score Matching

Efficient Low Rank Gaussian Variational Inference for Neural Networks

Efficient Marginalization of Discrete and Structured Latent Variables via Sparsity

Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning

Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees

Efficient Online Learning of Optimal Rankings: Dimensionality Reduction via Gradient Descent

Efficient semidefinite-programming-based inference for binary and multi-class MRFs

Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee

Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Class-Imbalanced Data

Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design

Emergent Reciprocity and Team Formation from Randomized Uncertain Social Preferences

Empirical Likelihood for Contextual Bandits

Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming

Energy-based Out-of-distribution Detection

Ensemble Distillation for Robust Model Fusion in Federated Learning

Ensembling geophysical models with Bayesian Neural Networks

Ensuring Fairness Beyond the Training Data

Entropic Optimal Transport between (Unbalanced) Gaussian Measures has a Closed Form

Entrywise convergence of iterative methods for eigenproblems

Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs

Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks

Estimating Training Data Influence by Tracing Gradient Descent

Estimation and imputation in Probabilistic Principal Component Analysis with Missing Not At Random data

Evaluating Attribution for Graph Neural Networks

Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational Autoencoders

Evolving Normalization-Activation Layers

Exactly Computing the Local Lipschitz Constant of ReLU Networks

Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation

Experimental design for MRI by greedy policy search

Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation

Factorizable Graph Convolutional Networks

Fair Multiple Decision Making Through Soft Interventions

Fairness in Streaming Submodular Maximization: Algorithms and Hardness

Fairness without Demographics through Adversarially Reweighted Learning

Faithful Embeddings for Knowledge Base Queries

Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation

Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms

Fast and Flexible Temporal Point Processes with Triangular Maps

Fast Epigraphical Projection-based Incremental Algorithms for Wasserstein Distributionally Robust Support Vector Machine

Fast Fourier Convolution

Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization

Fast Transformers with Clustered Attention

Fast Unbalanced Optimal Transport on a Tree

Feature Importance Ranking for Deep Learning

Feature Shift Detection: Localizing Which Features Have Shifted via Conditional Distribution Tests

Federated Accelerated Stochastic Gradient Descent

Federated Bayesian Optimization via Thompson Sampling

Federated Principal Component Analysis

Few-Cost Salient Object Detection with Adversarial-Paced Learning

Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies

Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications

Field-wise Learning for Multi-field Categorical Data

Finding the Homology of Decision Boundaries with Active Learning

Fine-Grained Dynamic Head for Object Detection

Finer Metagenomic Reconstruction via Biodiversity Optimization

Finite Versus Infinite Neural Networks: an Empirical Study

Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks

First Order Constrained Optimization in Policy Space

FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence

Flexible mean field variational inference using mixtures of non-overlapping exponential families

Flows for simultaneous manifold learning and density estimation

Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability Volumes

Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains

FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training

From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering

Fully Convolutional Mesh Autoencoder using Efficient Spatially Varying Kernels

Functional Regularization for Representation Learning: A Unified Theoretical Perspective

Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing

Further Analysis of Outlier Detection with Deep Generative Models

GAIT-prop: A biologically plausible learning rule derived from backpropagation of error

Gamma-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction

GAN Memory with No Forgetting

GANSpace: Discovering Interpretable GAN Controls

Gaussian Gated Linear Networks

Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective

Generalization Bound of Gradient Descent for Non-Convex Metric Learning

Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection

Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning

Generative 3D Part Assembly via Dynamic Graph Learning

Generative causal explanations of black-box classifiers

Generative Neurosymbolic Machines

Generative View Synthesis: From Single-view Semantics to Novel-view Images

Geometric All-Way Boolean Tensor Decomposition

Geometric Dataset Distances via Optimal Transport

Geo-PIFu: Geometry and Pixel Aligned Implicit Functions for Single-view Human Reconstruction

Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification

Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search

GNNGuard: Defending Graph Neural Networks against Adversarial Attacks

GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network

GradAug: A New Regularization Method for Deep Neural Networks

Gradient Boosted Normalizing Flows

Gradient Estimation with Stochastic Softmax Tricks

Gradient Regularized V-Learning for Dynamic Treatment Regimes

Gradient Surgery for Multi-Task Learning

Graduated Assignment for Joint Multi-Graph Matching and Clustering with Application to Unsupervised Graph Matching Network Learning

GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis

Graph Contrastive Learning with Augmentations

Graph Cross Networks with Vertex Infomax Pooling

Graph Geometry Interaction Learning

Graph Information Bottleneck

Graph Policy Network for Transferable Active Learning on Graphs

Graph Random Neural Networks for Semi-Supervised Learning on Graphs

Graph Stochastic Neural Networks for Semi-supervised Learning

Grasp Proposal Networks: An End-to-End Solution for Visual Learning of Robotic Grasps

GreedyFool: Distortion-Aware Sparse Adversarial Attack

Greedy inference with structure-exploiting lazy maps

Greedy Optimization Provably Wins the Lottery: Logarithmic Number of Winning Tickets is Enough

Group Contextual Encoding for 3D Point Clouds

Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge

GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators

Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses

Guiding Deep Molecular Optimization with Genetic Exploration

Hard Shape-Constrained Kernel Machines

Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks

HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks

Heuristic Domain Adaptation

Hierarchically Organized Latent Modules for Exploratory Search in Morphogenetic Systems

Hierarchical Neural Architecture Search for Deep Stereo Matching

Hierarchical nucleation in deep neural networks

Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample

Hierarchical Quantized Autoencoders

High-contrast “gaudy” images improve the training of deep neural network models of visual cortex

High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds

High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization

High-Fidelity Generative Image Compression

High-recall causal discovery for autocorrelated time series with latent confounders

High-Throughput Synchronous Deep RL

HiPPO: Recurrent Memory with Optimal Polynomial Projections

H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks

HOI Analysis: Integrating and Decomposing Human-Object Interaction

Hold me tight! Influence of discriminative features on deep network boundaries

How Can I Explain This to You? An Empirical Study of Deep Neural Network Explanation Methods

How Do Fair Decisions Fare in Long-term Qualification?

How to Characterize The Landscape of Overparameterized Convolutional Neural Networks

How to Learn a Useful Critic? Model-based Action-Gradient-Estimator Policy Optimization

HRN: A Holistic Approach to One Class Learning

Human Parsing Based Texture Transfer from Single Image to 3D Human via Cross-View Consistency

Hybrid Models for Learning to Branch

HYDRA: Pruning Adversarially Robust Neural Networks

HyNet: Learning Local Descriptor with Hybrid Similarity Measure and Triplet Loss

Hyperparameter Ensembles for Robustness and Uncertainty Quantification

Hypersolvers: Toward Fast Continuous-Depth Models

ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping

ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA

ICNet: Intra-saliency Correlation Network for Co-Saliency Detection

Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models

Identifying Learning Rules From Neural Network Observables

Identifying Mislabeled Data using the Area Under the Margin Ranking

Implicit Distributional Reinforcement Learning

Implicit Graph Neural Networks

Implicit Neural Representations with Periodic Activation Functions

Implicit Rank-Minimizing Autoencoder

Implicit Regularization in Deep Learning May Not Be Explainable by Norms

Improved Analysis of Clipping Algorithms for Non-convex Optimization

Improved Schemes for Episodic Memory-based Lifelong Learning

Improved Techniques for Training Score-Based Generative Models

Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows

Improving Auto-Augment via Augmentation-Wise Weight Sharing

Improving GAN Training with Probability Ratio Clipping and Sample Reweighting

Improving Generalization in Reinforcement Learning with Mixture Regularization

Improving Inference for Neural Image Compression

Improving Local Identifiability in Probabilistic Box Embeddings

Improving model calibration with accuracy versus uncertainty optimization

Improving Neural Network Training in Low Dimensional Random Bases

Improving Policy-Constrained Kidney Exchange via Pre-Screening

Improving robustness against common corruptions by covariate shift adaptation

Incorporating BERT into Parallel Sequence Decoding with Adapters

Incorporating Interpretable Output Constraints in Bayesian Neural Networks

Inductive Quantum Embedding

Inferring learning rules from animal decision-making

Influence-Augmented Online Planning for Complex Environments

Information Maximization for Few-Shot Learning

Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback

Information Theoretic Regret Bounds for Online Nonlinear Control

Information-theoretic Task Selection for Meta-Reinforcement Learning

In Search of Robust Measures of Generalization

Instance Selection for GANs

Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients

Interferobot: aligning an optical interferometer by a reinforcement learning agent

Interior Point Solving for LP-based prediction+optimisation

Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs

Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations

Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding

Interventional Few-Shot Learning

Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks

Intra-Processing Methods for Debiasing Neural Networks

Inverse Learning of Symmetries

Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax

Inverting Gradients - How easy is it to break privacy in federated learning?

Is normalization indispensable for training deep neural network?

ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding

Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings

JAX MD: A Framework for Differentiable Physics

Joint Contrastive Learning with Infinite Possibilities

Joint Policy Search for Multi-agent Collaboration with Imperfect Information

Joints in Random Forests

Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout

Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks

Kernel methods through the roof: handling billions of points efficiently

Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control

Label-Aware Neural Tangent Kernel: Toward Better Generalization and Local Elasticity

Labelling unlabelled videos from scratch with multi-modal self-supervision

Lamina-specific neuronal properties promote robust, stable signal propagation in feedforward networks

Language and Visual Entity Relationship Graph for Agent Navigation

Language as a Cognitive Tool to Imagine Goals in Curiosity Driven Exploration

Language-Conditioned Imitation Learning for Robot Manipulation Tasks

Language Models are Few-Shot Learners

Language Through a Prism: A Spectral Approach for Multiscale Language Representations

LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-Resolution and Beyond

Large-Scale Adversarial Training for Vision-and-Language Representation Learning

Large-Scale Methods for Distributionally Robust Optimization

Latent Dynamic Factor Analysis of High-Dimensional Neural Recordings

Latent Template Induction with Gumbel-CRFs

Latent World Models For Intrinsically Motivated Exploration

Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge

Learning Affordance Landscapes for Interaction Exploration in 3D Environments

Learning Augmented Energy Minimization via Speed Scaling

Learning Black-Box Attackers with Transferable Priors and Query Feedback

Learning Bounds for Risk-sensitive Learning

Learning by Minimizing the Sum of Ranked Range

Learning Certified Individually Fair Representations

Learning compositional functions via multiplicative weight updates

Learning Compositional Rules via Neural Program Synthesis

Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations

Learning Deformable Tetrahedral Meshes for 3D Reconstruction

Learning Differential Equations that are Easy to Solve

Learning Disentangled Representations and Group Structure of Dynamical Environments

Learning Disentangled Representations of Videos with Missing Data

Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction

Learning Dynamic Belief Graphs to Generalize on Text-Based Games

Learning efficient task-dependent representations with synaptic plasticity

Learning Feature Sparse Principal Subspace

Learning from Aggregate Observations

Learning from Positive and Unlabeled Data with Arbitrary Positive Shift

Learning Graph Structure With A Finite-State Automaton Layer

Learning Guidance Rewards with Trajectory-space Smoothing

Learning Heuristics over Large Graphs via Deep Reinforcement Learning

Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE

Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning

Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence

Learning Individually Inferred Communication for Multi-Agent Cooperation

Learning Invariants through Soft Unification

Learning Kernel Tests Without Data Splitting

Learning Latent Space Energy-Based Prior Model

Learning Long-Term Dependencies in Irregularly-Sampled Time Series

Learning Loss for Test-Time Augmentation

Learning Multi-Agent Communication through Structured Attentive Reasoning

Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks

Learning Mutational Semantics

Learning Object-Centric Representations of Multi-Object Scenes from Multiple Views

Learning of Discrete Graphical Models with Neural Networks

Learning Optimal Representations with the Decodable Information Bottleneck

Learning outside the Black-Box: The pursuit of interpretable models

Learning Physical Constraints with Neural Projections

Learning Physical Graph Representations from Visual Scenes

Learning Retrospective Knowledge with Reverse Reinforcement Learning

Learning Rich Rankings

Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search

Learning Semantic-aware Normalization for Generative Adversarial Networks

Learning sparse codes from compressed representations with biologically plausible local wiring constraints

Learning Sparse Prototypes for Text Generation

Learning Structured Distributions From Untrusted Batches: Faster and Simpler

Learning the geometry of wave-based imaging

Learning to Adapt to Evolving Domains

Learning to Approximate a Bregman Divergence

Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning

Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks

Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction

Learning to Incentivize Other Learning Agents

Learning to Learn Variational Semantic Memory

Learning to Orient Surfaces by Self-supervised Spherical CNNs

Learning to Play No-Press Diplomacy with Best Response Policy Iteration

Learning to Prove Theorems by Learning to Generate Theorems

Learning to search efficiently for causally near-optimal treatments

Learning to solve TV regularised problems with unrolled algorithms

Learning to summarize with human feedback

Learning under Model Misspecification: Applications to Variational and Ensemble methods

Learning with Operator-valued Kernels in Reproducing Kernel Krein Spaces

Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting

Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation

Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder

Linearly Converging Error Compensated SGD

Linear Time Sinkhorn Divergences using Positive Features

Lipschitz-Certifiable Training with a Tight Outer Bound

Listening to Sounds of Silence for Speech Denoising

Locally Differentially Private (Contextual) Bandits Learning

Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment

Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors

Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect

Look-ahead Meta Learning for Continual Learning

Make One-Shot Video Object Segmentation Efficient Again

Manifold GPLVMs for discovering non-Euclidean latent structure in neural data

MATE: Plugging in Model Awareness to Task Embedding for Meta Learning

Matérn Gaussian processes on Riemannian manifolds

Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copula

Matrix Inference and Estimation in Multi-Layer Models

Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness

MCUNet: Tiny Deep Learning on IoT Devices

MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning

Measuring Robustness to Natural Distribution Shifts in Image Classification

Measuring Systematic Generalization in Neural Proof Generation with Transformers

Memory-Efficient Learning of Stable Linear Dynamical Systems for Prediction and Control

MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler

MeshSDF: Differentiable Iso-Surface Extraction

Meta-Consolidation for Continual Learning

Meta-learning from Tasks with Heterogeneous Attribute Spaces

Meta-Learning Requires Meta-Augmentation

Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes

Meta-Learning through Hebbian Plasticity in Random Networks

Meta-Learning with Adaptive Hyperparameters

Meta-Neighborhoods

MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures

MetaPoison: Practical General-purpose Clean-label Data Poisoning

MetaSDF: Meta-learning Signed Distance Functions

MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers

Minimax Classification with 0-1 Loss and Performance Guarantees

Minimax Dynamics of Optimally Balanced Spiking Networks of Excitatory and Inhibitory Neurons

Minimax Estimation of Conditional Moment Models

Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks

MinMax Methods for Optimal Transport and Beyond: Regularization, Approximation and Numerics

Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments

Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions

Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous Variables

MMA Regularization: Decorrelating Weights of Neural Networks by Maximizing the Minimal Angles

Model-based Adversarial Meta-Reinforcement Learning

Model-based Policy Optimization with Unsupervised Model Adaptation

Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs

Model Class Reliance for Random Forests

Model Fusion via Optimal Transport

Modeling and Optimization Trade-off in Meta-learning

Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows

Modeling Shared Responses in Neuroimaging Studies through MultiView ICA

Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction

Model Rubik’s Cube: Twisting Resolution, Depth and Width for TinyNets

Modern Hopfield Networks and Attention for Immune Repertoire Classification

MomentumRNN: Integrating Momentum into Recurrent Neural Networks

Monotone operator equilibrium networks

MOPO: Model-based Offline Policy Optimization

Movement Pruning: Adaptive Sparsity by Fine-Tuning

MPNet: Masked and Permuted Pre-training for Language Understanding

MRI Banding Removal via Adversarial Training

Multi-agent active perception with prediction rewards

Multi-agent Trajectory Prediction with Fuzzy Query Attention

Multilabel Classification by Hierarchical Partitioning and Data-dependent Grouping

Multi-label classification: do Hamming loss and subset accuracy really conflict with each other?

Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence

Multiparameter Persistence Image for Topological Machine Learning

Multipole Graph Neural Operator for Parametric Partial Differential Equations

Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates

Multiscale Deep Equilibrium Models

Multi-task Causal Learning with Gaussian Processes

Multi-Task Reinforcement Learning with Soft Modularization

Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement

Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance

Munchausen Reinforcement Learning

NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity

Network Diffusions via Neural Mean-Field Dynamics

Network-to-Network Translation with Conditional Invertible Neural Networks

Neural Anisotropy Directions

Neural Architecture Generator Optimization

Neural Complexity Measures

Neural Controlled Differential Equations for Irregular Time Series

Neural encoding with visual attention

Neural Execution Engines: Learning to Execute Subroutines

Neural Manifold Ordinary Differential Equations

Neural Mesh Flow: 3D Manifold Mesh Generation via Diffeomorphic Flows

Neural Methods for Point-wise Dependency Estimation

Neural Networks Fail to Learn Periodic Functions and How to Fix It

Neural Networks with Recurrent Generative Feedback

Neural Non-Rigid Tracking

Neural Power Units

Neural Sparse Representation for Image Restoration

Neural Sparse Voxel Fields

Neural Unsigned Distance Fields for Implicit Function Learning

Neuronal Gaussian Process Regression

Neuron-level Structured Pruning using Polarization Regularizer

Neuron Merging: Compensating for Pruned Neurons

Neuron Shapley: Discovering the Responsible Neurons

Neurosymbolic Reinforcement Learning with Formally Verified Exploration

Neurosymbolic Transformers for Multi-Agent Communication

Neutralizing Self-Selection Bias in Sampling for Sortition

Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning

Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding

Node Embeddings and Exact Low-Rank Representations of Complex Networks

Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising

Noise-Contrastive Estimation for Multivariate Point Processes

Non-Euclidean Universal Approximation

No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems

Novelty Search in Representational Space for Sample Efficient Exploration

Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning

NVAE: A Deep Hierarchical Variational Autoencoder

Object-Centric Learning with Slot Attention

Object Goal Navigation using Goal-Oriented Semantic Exploration

Off-Policy Evaluation and Learning for External Validity under a Covariate Shift

Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding

On 1/n neural representation and robustness

On Adaptive Attacks to Adversarial Example Defenses

Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free

On Completeness-aware Concept-Based Explanations in Deep Neural Networks

One-bit Supervision for Image Classification

One Ring to Rule Them All: Certifiably Robust Geometric Perception with Outliers

On Infinite-Width Hypernetworks

Online Algorithms for Multi-shop Ski Rental with Machine Learned Advice

Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning

Online Meta-Critic Learning for Off-Policy Actor-Critic Methods

Online neural connectivity estimation with ensemble stimulation

Online Optimization with Memory and Competitive Control

On Power Laws in Deep Ensembles

On Second Order Behaviour in Augmented Neural ODEs

On the distance between two neural networks and the stability of learning

On the equivalence of molecular graph convolution and molecular wave function with poor basis set

On the Expressiveness of Approximate Inference in Bayesian Neural Networks

On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them

On the Modularity of Hypernetworks

On the Role of Sparsity and DAG Constraints for Learning Linear DAGs

On the Similarity between the Laplace and Neural Tangent Kernels

On the Trade-off between Adversarial and Backdoor Robustness

On the training dynamics of deep networks with L 2 regularization

On Warm-Starting Neural Network Training

Open Graph Benchmark: Datasets for Machine Learning on Graphs

Optimal Adaptive Electrode Selection to Maximize Simultaneously Recorded Neuron Yield

Optimal Lottery Tickets via Subset Sum: Logarithmic Over-Parameterization is Sufficient

Optimal Variance Control of the Score-Function Gradient Estimator for Importance-Weighted Bounds

Optimal visual search based on a model of target detectability in natural images

Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks

Optimizing Mode Connectivity via Neuron Alignment

Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree

Parabolic Approximation Line Search for DNNs

Parameterized Explainer for Graph Neural Network

Part-dependent Label Noise: Towards Instance-dependent Label Noise

Passport-aware Normalization for Deep Model Protection

Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning​

Path Integral Based Convolution and Pooling for Graph Neural Networks

PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning

Personalized Federated Learning with Moreau Envelopes

Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach

PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks

Phase retrieval in high dimensions: Statistical and computational phase transitions

Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games

Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation

PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals

PLANS: Robust Program Learning from Neurally Inferred Specifications

PLLay: Efficient Topological Layer based on Persistence Landscapes

PLLay: Efficient Topological Layer based on Persistent Landscapes

Point process models for sequence detection in high-dimensional neural spike trains

POMO: Policy Optimization with Multiple Optima for Reinforcement Learning

Pontryagin Differentiable Programming: An End-to-End Learning and Control Framework

Position-based Scaled Gradient for Model Quantization and Pruning

Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts

Practical Low-Rank Communication Compression in Decentralized Deep Learning

Practical No-box Adversarial Attacks against DNNs

Practical Quasi-Newton Methods for Training Deep Neural Networks

Predictive Information Accelerates Learning in RL

Pre-training via Paraphrasing

Primal-Dual Mesh Convolutional Neural Networks

Principal Neighbourhood Aggregation for Graph Nets

Probabilistic Active Meta-Learning

Probabilistic Circuits for Variational Inference in Discrete Graphical Models

Probabilistic Linear Solvers for Machine Learning

Probabilistic orientation estimation with matrix Fisher distributions

Probabilistic Time Series Forecasting with Shape and Temporal Diversity

Projected Stein Variational Gradient Descent

Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning

Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning

Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits

Provably Robust Metric Learning

Proximal Mapping for Deep Regularization

Pruning Filter in Filter

Pruning neural networks without any data by iteratively conserving synaptic flow

Quantile Propagation for Wasserstein-Approximate Gaussian Processes

Quantized Variational Inference

RandAugment: Practical automated data augmentation with a reduced search space

Random Reshuffling: Simple Analysis with Vast Improvements

RANet: Region Attention Network for Semantic Segmentation

Rational neural networks

RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning

Real World Games Look Like Spinning Tops

Reciprocal Adversarial Learning via Characteristic Functions

Reconsidering Generative Objectives For Counterfactual Reasoning

Reconstructing Perceptive Images from Brain Activity by Shape-Semantic GAN

Recurrent Quantum Neural Networks

Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization

Regularized linear autoencoders recover the principal components, eventually

Regularizing Black-box Models for Improved Interpretability

Reinforced Molecular Optimization with Neighborhood-Controlled Grammars

Reinforcement Learning with Augmented Data

Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing

Rel3D: A Minimally Contrastive Benchmark for Grounding Spatial Relations in 3D

RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces

RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder

Relative gradient optimization of the Jacobian term in unsupervised deep learning

Reliable Graph Neural Networks via Robust Aggregation

Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies

RepPoints V2: Verification Meets Regression for Object Detection

Rescuing neural spike train models from bad MLE

Reservoir Computing meets Recurrent Kernels and Structured Transforms

Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis

Restoring Negative Information in Few-Shot Object Detection

Rethinking Importance Weighting for Deep Learning under Distribution Shift

Rethinking Learnable Tree Filter for Generic Feature Transform

Rethinking pooling in graph neural networks

Rethinking Pre-training and Self-training

Rethinking the Value of Labels for Improving Class-Imbalanced Learning

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

RetroXpert: Decompose Retrosynthesis Prediction like a Chemist

Revisiting Parameter Sharing for Automatic Neural Channel Number Search

Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes

Reward Propagation Using Graph Convolutional Networks

Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement

RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning

RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference

Robust Compressed Sensing using Generative Models

Robust Correction of Sampling Bias Using Cumulative Distribution Functions

Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations

Robust Disentanglement of a Few Factors at a Time

Robustness of Bayesian Neural Networks to Gradient-Based Attacks

Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation

Robust Optimization for Fairness with Noisy Protected Groups

Robust Persistence Diagrams using Reproducing Kernels

Robust Pre-Training by Adversarial Contrastive Learning

Robust Quantization: One Model to Rule Them All

Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-parameterization

Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification

Rotated Binary Neural Network

RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor

Safe Reinforcement Learning via Curriculum Induction

Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining

Sampling from a k-DPP without looking at all items

Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot

Scalable Graph Neural Networks via Bidirectional Propagation

Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks

SCOP: Scientific Control for Reliable Neural Network Pruning

SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images

SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks

Searching for Low-Bit Weights in Quantized Neural Networks

Second Order PAC-Bayesian Bounds for the Weighted Majority Vote

See, Hear, Explore: Curiosity via Audio-Visual Association

Self-Adaptive Training: beyond Empirical Risk Minimization

Self-Distillation as Instance-Specific Label Smoothing

Self-Learning Transformations for Improving Gaze and Head Redirection

Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID

Self-Paced Deep Reinforcement Learning

Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs

Self-supervised Co-training for Video Representation Learning

Self-Supervised Few-Shot Learning on Point Clouds

Self-Supervised Graph Transformer on Large-Scale Molecular Data

Self-Supervised Learning by Cross-Modal Audio-Video Clustering

Self-supervised learning through the eyes of a child

Self-Supervised MultiModal Versatile Networks

Self-Supervised Relational Reasoning for Representation Learning

Semantic Visual Navigation by Watching YouTube Videos

Semi-Supervised Neural Architecture Search

Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding

Set2Graph: Learning Graphs From Sets

SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology

Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning

ShiftAddNet: A Hardware-Inspired Deep Network

Simple and Fast Algorithm for Binary Integer and Online Linear Programming

Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness

Simple and Scalable Sparse k-means Clustering via Feature Ranking

Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering

Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints

Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations

Sliding Window Algorithms for k-Clustering Problems

SLIP: Learning to Predict in Unknown Dynamical Systems with Long-Term Memory

Smoothed Geometry for Robust Attribution

SMYRF - Efficient Attention using Asymmetric Clustering

SnapBoost: A Heterogeneous Boosting Machine

Soft Contrastive Learning for Visual Localization

SoftFlow: Probabilistic Framework for Normalizing Flow on Manifolds

Softmax Deep Double Deterministic Policy Gradients

SOLOv2: Dynamic and Fast Instance Segmentation

Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers

Space-Time Correspondence as a Contrastive Random Walk

Sparse and Continuous Attention Mechanisms

Sparse Graphical Memory for Robust Planning

Sparse Symplectically Integrated Neural Networks

Sparse Weight Activation Training

Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting

Spin-Weighted Spherical CNNs

Stable and expressive recurrent vision models

Stationary Activations for Uncertainty Calibration in Deep Learning

Statistical and Topological Properties of Sliced Probability Divergences

Stein Self-Repulsive Dynamics: Benefits From Past Samples

Stochastic Deep Gaussian Processes over Graphs

Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model

Stochastic Normalization

Stochastic Normalizing Flows

Stochastic Optimization for Performative Prediction

Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping

Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty

Stochastic Stein Discrepancies

Strictly Batch Imitation Learning by Energy-based Distribution Matching

Strongly Incremental Constituency Parsing with Graph Neural Networks

Strongly local p-norm-cut algorithms for semi-supervised learning and local graph clustering

Structured Prediction for Conditional Meta-Learning

Subgraph Neural Networks

Submodular Meta-Learning

Sub-sampling for Efficient Non-Parametric Bandit Exploration

Succinct and Robust Multi-Agent Communication With Temporal Message Control

Sufficient dimension reduction for classification using principal optimal transport direction

SuperLoss: A Generic Loss for Robust Curriculum Learning

Supermasks in Superposition

Supervised Contrastive Learning

SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows

Swapping Autoencoder for Deep Image Manipulation

Synbols: Probing Learning Algorithms with Synthetic Datasets

Synthesize, Execute and Debug: Learning to Repair for Neural Program Synthesis

Synthesizing Tasks for Block-based Programming

System Identification with Biophysical Constraints: A Circuit Model of the Inner Retina

Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization

Taming Discrete Integration via the Boon of Dimensionality

Targeted Adversarial Perturbations for Monocular Depth Prediction

Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters

Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes

Task-Oriented Feature Distillation

TaylorGAN: Neighbor-Augmented Policy Update Towards Sample-Efficient Natural Language Generation

Teaching a GAN What Not to Learn

Telescoping Density-Ratio Estimation

Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks

Texture Interpolation for Probing Visual Perception

The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning

The Complete Lasso Tradeoff Diagram

The Cone of Silence: Speech Separation by Localization

The Convolution Exponential and Generalized Sylvester Flows

The Depth-to-Width Interplay in Self-Attention

The Dilemma of TriHard Loss and an Element-Weighted TriHard Loss for Person Re-Identification

The Discrete Gaussian for Differential Privacy

The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes

The interplay between randomness and structure during learning in RNNs

The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning

The Lottery Ticket Hypothesis for Pre-trained BERT Networks

The MAGICAL Benchmark for Robust Imitation

The Mean-Squared Error of Double Q-Learning

The NetHack Learning Environment

Theory-Inspired Path-Regularized Differential Network Architecture Search

The Pitfalls of Simplicity Bias in Neural Networks

The Potts-Ising model for discrete multivariate data

The Primal-Dual method for Learning Augmented Algorithms

The Strong Screening Rule for SLOPE

Throughput-Optimal Topology Design for Cross-Silo Federated Learning

Thunder: a Fast Coordinate Selection Solver for Sparse Learning

Time-Reversal Symmetric ODE Network

TinyTL: Reduce Activations, Not Trainable Parameters for Efficient On-Device Learning

Top-KAST: Top-K Always Sparse Training

Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples

TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search

Towards a Better Global Loss Landscape of GANs

Towards Crowdsourced Training of Large Neural Networks using Decentralized Mixture-of-Experts

Towards Deeper Graph Neural Networks with Differentiable Group Normalization

Towards Interpretable Natural Language Understanding with Explanations as Latent Variables

Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples

Towards More Practical Adversarial Attacks on Graph Neural Networks

Towards Neural Programming Interfaces

Towards Safe Policy Improvement for Non-Stationary MDPs

Trading Personalization for Accuracy: Data Debugging in Collaborative Filtering

Train-by-Reconnect: Decoupling Locations of Weights from their Values

Training Generative Adversarial Networks by Solving Ordinary Differential Equations

Training Generative Adversarial Networks with Limited Data

Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification

Training Stronger Baselines for Learning to Optimize

Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning

Tree! I am no Tree! I am a Low Dimensional Hyperbolic Embedding

Triple descent and the two kinds of overfitting: Where & why do they appear?

TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translation

UCSG-NET- Unsupervised Discovering of Constructive Solid Geometry Tree

UDH: Universal Deep Hiding for Steganography, Watermarking, and Light Field Messaging

Ultrahyperbolic Representation Learning

Unbalanced Sobolev Descent

Uncertainty Aware Semi-Supervised Learning on Graph Data

Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence

Understanding and Exploring the Network with Stochastic Architectures

Understanding and Improving Fast Adversarial Training

Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features

Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks

Understanding Deep Architecture with Reasoning Layer

Understanding Global Feature Contributions With Additive Importance Measures

Understanding spiking networks through convex optimization

Understanding the Role of Training Regimes in Continual Learning

Unfolding the Alternating Optimization for Blind Super Resolution

Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks

Universal Domain Adaptation through Self Supervision

Universal Function Approximation on Graphs

Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms

Unsupervised Data Augmentation for Consistency Training

Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control

Unsupervised Learning of Object Landmarks via Self-Training Correspondence

Unsupervised Learning of Visual Features by Contrasting Cluster Assignments

Unsupervised object-centric video generation and decomposition in 3D

Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning

Unsupervised Translation of Programming Languages

User-Dependent Neural Sequence Models for Continuous-Time Event Data

UWSOD: Toward Fully-Supervised-Level Capacity Weakly Supervised Object Detection

VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data

VarGrad: A Low-Variance Gradient Estimator for Variational Inference

Variational Bayesian Monte Carlo with Noisy Likelihoods

Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings

Variational Interaction Information Maximization for Cross-domain Disentanglement

Video Frame Interpolation without Temporal Priors

Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement

VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain

Walking in the Shadow: A New Perspective on Descent Directions for Constrained Minimization

Wasserstein Distances for Stereo Disparity Estimation

Watch out! Motion is Blurring the Vision of Your Deep Neural Networks

wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations

Wavelet Flow: Fast Training of High Resolution Normalizing Flows

Weak Form Generalized Hamiltonian Learning

Weakly Supervised Deep Functional Maps for Shape Matching

Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings

What Did You Think Would Happen? Explaining Agent Behaviour Through Intended Outcomes

What if Neural Networks had SVDs?

What is being transferred in transfer learning?

What Makes for Good Views for Contrastive Learning?

When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes

When Counterpoint Meets Chinese Folk Melodies

When Do Neural Networks Outperform Kernel Methods?

Why Normalizing Flows Fail to Detect Out-of-Distribution Data

Winning the Lottery with Continuous Sparsification

Wisdom of the Ensemble: Improving Consistency of Deep Learning Models

Woodbury Transformations for Deep Generative Flows

WoodFisher: Efficient Second-Order Approximation for Neural Network Compression

Worst-Case Analysis for Randomly Collected Data

X-CAL: Explicit Calibration for Survival Analysis

Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling

Zero-Resource Knowledge-Grounded Dialogue Generation