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UMEC: Unified model and embedding compression for efficient recommendation systems
Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification
ResNet After All: Neural ODEs and Their Numerical Solution
Deep Partition Aggregation: Provable Defenses against General Poisoning Attacks
MetaNorm: Learning to Normalize Few-Shot Batches Across Domains
Fidelity-based Deep Adiabatic Scheduling
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching
CPT: Efficient Deep Neural Network Training via Cyclic Precision
Tilted Empirical Risk Minimization
Improved Estimation of Concentration Under $\ell_p$-Norm Distance Metrics Using Half Spaces
ALFWorld: Aligning Text and Embodied Environments for Interactive Learning
SEDONA: Search for Decoupled Neural Networks toward Greedy Block-wise Learning
Learning to Generate 3D Shapes with Generative Cellular Automata
Sliced Kernelized Stein Discrepancy
Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation
VA-RED$^2$: Video Adaptive Redundancy Reduction
On InstaHide, Phase Retrieval, and Sparse Matrix Factorization
Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity
Statistical inference for individual fairness
Emergent Symbols through Binding in External Memory
Early Stopping in Deep Networks: Double Descent and How to Eliminate it
On the Universality of the Double Descent Peak in Ridgeless Regression
Evaluation of Neural Architectures Trained With Square Loss vs Cross-Entropy in Classification Tasks
Explainable Subgraph Reasoning for Forecasting on Temporal Knowledge Graphs
Simple Spectral Graph Convolution
PolarNet: Learning to Optimize Polar Keypoints for Keypoint Based Object Detection
Deconstructing the Regularization of BatchNorm
RMSprop converges with proper hyper-parameter
Generative Scene Graph Networks
Learnable Embedding sizes for Recommender Systems
Overfitting for Fun and Profit: Instance-Adaptive Data Compression
Acting in Delayed Environments with Non-Stationary Markov Policies
ARMOURED: Adversarially Robust MOdels using Unlabeled data by REgularizing Diversity
Solving Compositional Reinforcement Learning Problems via Task Reduction
A Geometric Analysis of Deep Generative Image Models and Its Applications
A Discriminative Gaussian Mixture Model with Sparsity
Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels
Certify or Predict: Boosting Certified Robustness with Compositional Architectures
Taming GANs with Lookahead-Minmax
Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis
Learning Subgoal Representations with Slow Dynamics
GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images
CO2: Consistent Contrast for Unsupervised Visual Representation Learning
CPR: Classifier-Projection Regularization for Continual Learning
MARS: Markov Molecular Sampling for Multi-objective Drug Discovery
Fooling a Complete Neural Network Verifier
Representation Learning via Invariant Causal Mechanisms
Interpreting and Boosting Dropout from a Game-Theoretic View
Quantifying Differences in Reward Functions
BOIL: Towards Representation Change for Few-shot Learning
Generating Adversarial Computer Programs using Optimized Obfuscations
On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis
Stabilized Medical Image Attacks
FOCAL: Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior Regularization
Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections
Memory Optimization for Deep Networks
Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filtering
Identifying nonlinear dynamical systems with multiple time scales and long-range dependencies
Hyperbolic Neural Networks++
Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies
Spatially Structured Recurrent Modules
Teaching Temporal Logics to Neural Networks
Self-supervised Visual Reinforcement Learning with Object-centric Representations
On Self-Supervised Image Representations for GAN Evaluation
Robust Learning of Fixed-Structure Bayesian Networks in Nearly-Linear Time
Reset-Free Lifelong Learning with Skill-Space Planning
Fast Geometric Projections for Local Robustness Certification
TropEx: An Algorithm for Extracting Linear Terms in Deep Neural Networks
Adapting to Reward Progressivity via Spectral Reinforcement Learning
Decentralized Attribution of Generative Models
Combining Physics and Machine Learning for Network Flow Estimation
Large Batch Simulation for Deep Reinforcement Learning
Byzantine-Resilient Non-Convex Stochastic Gradient Descent
Accurate Learning of Graph Representations with Graph Multiset Pooling
GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing
Improving Zero-Shot Voice Style Transfer via Disentangled Representation Learning
HyperDynamics: Meta-Learning Object and Agent Dynamics with Hypernetworks
Anytime Sampling for Autoregressive Models via Ordered Autoencoding
Disentangling 3D Prototypical Networks for Few-Shot Concept Learning
Iterated learning for emergent systematicity in VQA
Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning
Generating Furry Cars: Disentangling Object Shape and Appearance across Multiple Domains
Why Are Convolutional Nets More Sample-Efficient than Fully-Connected Nets?
Learning to Make Decisions via Submodular Regularization
Adaptive and Generative Zero-Shot Learning
CompOFA – Compound Once-For-All Networks for Faster Multi-Platform Deployment
LambdaNetworks: Modeling long-range Interactions without Attention
Orthogonalizing Convolutional Layers with the Cayley Transform
Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs
Gradient Projection Memory for Continual Learning
More or Less: When and How to Build Convolutional Neural Network Ensembles
Efficient Empowerment Estimation for Unsupervised Stabilization
A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network Inference
Combining Ensembles and Data Augmentation Can Harm Your Calibration
Fourier Neural Operator for Parametric Partial Differential Equations
Saliency is a Possible Red Herring When Diagnosing Poor Generalization
Provably robust classification of adversarial examples with detection
SAFENet: A Secure, Accurate and Fast Neural Network Inference
MONGOOSE: A Learnable LSH Framework for Efficient Neural Network Training
Improved Autoregressive Modeling with Distribution Smoothing
Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning
Combining Label Propagation and Simple Models out-performs Graph Neural Networks
Local Search Algorithms for Rank-Constrained Convex Optimization
Pre-training Text-to-Text Transformers for Concept-centric Common Sense
Decoupling Global and Local Representations via Invertible Generative Flows
SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing
Evaluating the Disentanglement of Deep Generative Models through Manifold Topology
End-to-End Egospheric Spatial Memory
Neural Approximate Sufficient Statistics for Implicit Models
Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification
BREEDS: Benchmarks for Subpopulation Shift
PAC Confidence Predictions for Deep Neural Network Classifiers
Dance Revolution: Long-Term Dance Generation with Music via Curriculum Learning
Hopper: Multi-hop Transformer for Spatiotemporal Reasoning
Why resampling outperforms reweighting for correcting sampling bias with stochastic gradients
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning
Vector-output ReLU Neural Network Problems are Copositive Programs: Convex Analysis of Two Layer Networks and Polynomial-time Algorithms
AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition
Dataset Meta-Learning from Kernel Ridge-Regression
Repurposing Pretrained Models for Robust Out-of-domain Few-Shot Learning
On Position Embeddings in BERT
Contrastive Explanations for Reinforcement Learning via Embedded Self Predictions
Sequential Density Ratio Estimation for Simultaneous Optimization of Speed and Accuracy
Uncertainty Sets for Image Classifiers using Conformal Prediction
Conditional Negative Sampling for Contrastive Learning of Visual Representations
Faster Binary Embeddings for Preserving Euclidean Distances
Model-Based Offline Planning
Neural Networks for Learning Counterfactual G-Invariances from Single Environments
Learning Energy-Based Models by Diffusion Recovery Likelihood
QPLEX: Duplex Dueling Multi-Agent Q-Learning
Does enhanced shape bias improve neural network robustness to common corruptions?
OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning
PDE-Driven Spatiotemporal Disentanglement
Mapping the Timescale Organization of Neural Language Models
The Intrinsic Dimension of Images and Its Impact on Learning
Stochastic Security: Adversarial Defense Using Long-Run Dynamics of Energy-Based Models
CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackers
Long Range Arena : A Benchmark for Efficient Transformers
Recurrent Independent Mechanisms
Image GANs meet Differentiable Rendering for Inverse Graphics and Interpretable 3D Neural Rendering
SALD: Sign Agnostic Learning with Derivatives
WaveGrad: Estimating Gradients for Waveform Generation
Linear Last-iterate Convergence in Constrained Saddle-point Optimization
Go with the flow: Adaptive control for Neural ODEs
Understanding Over-parameterization in Generative Adversarial Networks
Multiscale Score Matching for Out-of-Distribution Detection
Random Feature Attention
Tradeoffs in Data Augmentation: An Empirical Study
Rapid Task-Solving in Novel Environments
Extracting Strong Policies for Robotics Tasks from Zero-Order Trajectory Optimizers
Unsupervised Discovery of 3D Physical Objects
Sample-Efficient Automated Deep Reinforcement Learning
Learning Structural Edits via Incremental Tree Transformations
Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis
Practical Real Time Recurrent Learning with a Sparse Approximation
Private Post-GAN Boosting
Modeling the Second Player in Distributionally Robust Optimization
HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark
Representation learning for improved interpretability and classification accuracy of clinical factors from EEG
GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
Multi-Level Local SGD: Distributed SGD for Heterogeneous Hierarchical Networks
R-GAP: Recursive Gradient Attack on Privacy
Share or Not? Learning to Schedule Language-Specific Capacity for Multilingual Translation
Isometric Transformation Invariant and Equivariant Graph Convolutional Networks
Chaos of Learning Beyond Zero-sum and Coordination via Game Decompositions
Grounding Language to Autonomously-Acquired Skills via Goal Generation
Trajectory Prediction using Equivariant Continuous Convolution
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels
On the role of planning in model-based deep reinforcement learning
A Hypergradient Approach to Robust Regression without Correspondence
Fast convergence of stochastic subgradient method under interpolation
Wasserstein Embedding for Graph Learning
Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization
Shape or Texture: Understanding Discriminative Features in CNNs
Neurally Augmented ALISTA
Learning from Demonstration with Weakly Supervised Disentanglement
Score-Based Generative Modeling through Stochastic Differential Equations
On Data-Augmentation and Consistency-Based Semi-Supervised Learning
Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch
The role of Disentanglement in Generalisation
Shapley Explanation Networks
C-Learning: Horizon-Aware Cumulative Accessibility Estimation
Multi-resolution modeling of a discrete stochastic process identifies causes of cancer
PC2WF: 3D Wireframe Reconstruction from Raw Point Clouds
Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learning
DINO: A Conditional Energy-Based GAN for Domain Translation
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients
AdaSpeech: Adaptive Text to Speech for Custom Voice
Simple Augmentation Goes a Long Way: ADRL for DNN Quantization
Into the Wild with AudioScope: Unsupervised Audio-Visual Separation of On-Screen Sounds
SkipW: Resource Adaptable RNN with Strict Upper Computational Limit
Pruning Neural Networks at Initialization: Why Are We Missing the Mark?
On the Origin of Implicit Regularization in Stochastic Gradient Descent
Transient Non-stationarity and Generalisation in Deep Reinforcement Learning
Adversarial score matching and improved sampling for image generation
LiftPool: Bidirectional ConvNet Pooling
Scalable Bayesian Inverse Reinforcement Learning
Return-Based Contrastive Representation Learning for Reinforcement Learning
Implicit Gradient Regularization
Variational Intrinsic Control Revisited
Understanding and Improving Lexical Choice in Non-Autoregressive Translation
Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models
Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets
Complex Query Answering with Neural Link Predictors
Learning to Sample with Local and Global Contexts in Experience Replay Buffer
Gradient Origin Networks
Nonseparable Symplectic Neural Networks
Revisiting Locally Supervised Learning: an Alternative to End-to-end Training
Deep Repulsive Clustering of Ordered Data Based on Order-Identity Decomposition
Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling
Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning
Reweighting Augmented Samples by Minimizing the Maximal Expected Loss
Robust early-learning: Hindering the memorization of noisy labels
Monte-Carlo Planning and Learning with Language Action Value Estimates
Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust Exploration
Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting
EigenGame: PCA as a Nash Equilibrium
DrNAS: Dirichlet Neural Architecture Search
Graph Edit Networks
Capturing Label Characteristics in VAEs
Neural Delay Differential Equations
A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning
Group Equivariant Stand-Alone Self-Attention For Vision
Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
Undistillable: Making A Nasty Teacher That CANNOT teach students
Learning Hyperbolic Representations of Topological Features
Lipschitz Recurrent Neural Networks
Explaining the Efficacy of Counterfactually Augmented Data
Behavioral Cloning from Noisy Demonstrations
Layer-adaptive Sparsity for the Magnitude-based Pruning
Prototypical Representation Learning for Relation Extraction
Learning Reasoning Paths over Semantic Graphs for Video-grounded Dialogues
Deformable DETR: Deformable Transformers for End-to-End Object Detection
When does preconditioning help or hurt generalization?
Group Equivariant Conditional Neural Processes
Learning from Protein Structure with Geometric Vector Perceptrons
PSTNet: Point Spatio-Temporal Convolution on Point Cloud Sequences
Anchor & Transform: Learning Sparse Embeddings for Large Vocabularies
Molecule Optimization by Explainable Evolution
Predicting Inductive Biases of Pre-Trained Models
Learning Better Structured Representations Using Low-rank Adaptive Label Smoothing
Deep Encoder, Shallow Decoder: Reevaluating Non-autoregressive Machine Translation
Multi-timescale Representation Learning in LSTM Language Models
Adaptive Procedural Task Generation for Hard-Exploration Problems
DeepAveragers: Offline Reinforcement Learning By Solving Derived Non-Parametric MDPs
Extreme Memorization via Scale of Initialization
Prototypical Contrastive Learning of Unsupervised Representations
Learning from others' mistakes: Avoiding dataset biases without modeling them
LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition
WaNet - Imperceptible Warping-based Backdoor Attack
Neural representation and generation for RNA secondary structures
Can a Fruit Fly Learn Word Embeddings?
RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs
Evaluations and Methods for Explanation through Robustness Analysis
gradSim: Differentiable simulation for system identification and visuomotor control
Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization
Isotropy in the Contextual Embedding Space: Clusters and Manifolds
Reinforcement Learning with Random Delays
Deep Learning meets Projective Clustering
Dual-mode ASR: Unify and Improve Streaming ASR with Full-context Modeling
On Graph Neural Networks versus Graph-Augmented MLPs
NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation
Distributional Sliced-Wasserstein and Applications to Generative Modeling
MoVie: Revisiting Modulated Convolutions for Visual Counting and Beyond
NOVAS: Non-convex Optimization via Adaptive Stochastic Search for End-to-end Learning and Control
Understanding the effects of data parallelism and sparsity on neural network training
Planning from Pixels using Inverse Dynamics Models
Benchmarks for Deep Off-Policy Evaluation
Meta-Learning of Structured Task Distributions in Humans and Machines
Growing Efficient Deep Networks by Structured Continuous Sparsification
Training independent subnetworks for robust prediction
Better Fine-Tuning by Reducing Representational Collapse
Selective Classification Can Magnify Disparities Across Groups
Zero-shot Synthesis with Group-Supervised Learning
Learning Task-General Representations with Generative Neuro-Symbolic Modeling
BERTology Meets Biology: Interpreting Attention in Protein Language Models
Mathematical Reasoning via Self-supervised Skip-tree Training
AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly
BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization
Economic Hyperparameter Optimization With Blended Search Strategy
Average-case Acceleration for Bilinear Games and Normal Matrices
Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network
IsarStep: a Benchmark for High-level Mathematical Reasoning
Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis
SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments
Genetic Soft Updates for Policy Evolution in Deep Reinforcement Learning
On the mapping between Hopfield networks and Restricted Boltzmann Machines
Distance-Based Regularisation of Deep Networks for Fine-Tuning
Ringing ReLUs: Harmonic Distortion Analysis of Nonlinear Feedforward Networks
Generalization in data-driven models of primary visual cortex
Efficient Continual Learning with Modular Networks and Task-Driven Priors
Implicit Convex Regularizers of CNN Architectures: Convex Optimization of Two- and Three-Layer Networks in Polynomial Time
Activation-level uncertainty in deep neural networks
On Statistical Bias In Active Learning: How and When to Fix It
Local Convergence Analysis of Gradient Descent Ascent with Finite Timescale Separation
Scaling the Convex Barrier with Active Sets
NAS-Bench-ASR: Reproducible Neural Architecture Search for Speech Recognition
PseudoSeg: Designing Pseudo Labels for Semantic Segmentation
Symmetry-Aware Actor-Critic for 3D Molecular Design
Long Live the Lottery: The Existence of Winning Tickets in Lifelong Learning
Robust Overfitting may be mitigated by properly learned smoothening
Characterizing signal propagation to close the performance gap in unnormalized ResNets
Learning continuous-time PDEs from sparse data with graph neural networks
Latent Skill Planning for Exploration and Transfer
Uncertainty-aware Active Learning for Optimal Bayesian Classifier
Self-supervised Adversarial Robustness for the Low-label, High-data Regime
Single-Photon Image Classification
Unsupervised Object Keypoint Learning using Local Spatial Predictability
CcGAN: Continuous Conditional Generative Adversarial Networks for Image Generation
Differentially Private Learning Needs Better Features (or Much More Data)
Plan-Based Relaxed Reward Shaping for Goal-Directed Tasks
ANOCE: Analysis of Causal Effects with Multiple Mediators via Constrained Structural Learning
Long-tailed Recognition by Routing Diverse Distribution-Aware Experts
Grounded Language Learning Fast and Slow
Transformer protein language models are unsupervised structure learners
Uncertainty Estimation in Autoregressive Structured Prediction
Learning to live with Dale's principle: ANNs with separate excitatory and inhibitory units
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
CT-Net: Channel Tensorization Network for Video Classification
On the Universality of Rotation Equivariant Point Cloud Networks
Universal approximation power of deep residual neural networks via nonlinear control theory
Learning a Latent Search Space for Routing Problems using Variational Autoencoders
A teacher-student framework to distill future trajectories
The Traveling Observer Model: Multi-task Learning Through Spatial Variable Embeddings
What they do when in doubt: a study of inductive biases in seq2seq learners
Group Equivariant Generative Adversarial Networks
Robust Curriculum Learning: from clean label detection to noisy label self-correction
Support-set bottlenecks for video-text representation learning
Graph Information Bottleneck for Subgraph Recognition
Learning Deep Features in Instrumental Variable Regression
Neural Synthesis of Binaural Speech From Mono Audio
Grounding Physical Concepts of Objects and Events Through Dynamic Visual Reasoning
Differentiable Segmentation of Sequences
Auto Seg-Loss: Searching Metric Surrogates for Semantic Segmentation
Network Pruning That Matters: A Case Study on Retraining Variants
Degree-Quant: Quantization-Aware Training for Graph Neural Networks
Boost then Convolve: Gradient Boosting Meets Graph Neural Networks
Learning Associative Inference Using Fast Weight Memory
SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization
Towards Robust Neural Networks via Close-loop Control
Differentiable Trust Region Layers for Deep Reinforcement Learning
Discovering Non-monotonic Autoregressive Orderings with Variational Inference
Rethinking Positional Encoding in Language Pre-training
PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics
Improving Relational Regularized Autoencoders with Spherical Sliced Fused Gromov Wasserstein
DiffWave: A Versatile Diffusion Model for Audio Synthesis
Calibration of Neural Networks using Splines
Exploring Balanced Feature Spaces for Representation Learning
Measuring Massive Multitask Language Understanding
Kanerva++: Extending the Kanerva Machine With Differentiable, Locally Block Allocated Latent Memory
Aligning AI With Shared Human Values
Learning Manifold Patch-Based Representations of Man-Made Shapes
Filtered Inner Product Projection for Crosslingual Embedding Alignment
Correcting experience replay for multi-agent communication
How Benign is Benign Overfitting ?
High-Capacity Expert Binary Networks
Structured Prediction as Translation between Augmented Natural Languages
Fuzzy Tiling Activations: A Simple Approach to Learning Sparse Representations Online
Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting
Incremental few-shot learning via vector quantization in deep embedded space
In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness
Reducing the Computational Cost of Deep Generative Models with Binary Neural Networks
MALI: A memory efficient and reverse accurate integrator for Neural ODEs
FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning
Rethinking the Role of Gradient-based Attribution Methods for Model Interpretability
My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control
Neural Learning of One-of-Many Solutions for Combinatorial Problems in Structured Output Spaces
Adaptive Universal Generalized PageRank Graph Neural Network
Latent Convergent Cross Mapping
Semantic Re-tuning with Contrastive Tension
On the Theory of Implicit Deep Learning: Global Convergence with Implicit Layers
GANs Can Play Lottery Tickets Too
Efficient Conformal Prediction via Cascaded Inference with Expanded Admission
Disambiguating Symbolic Expressions in Informal Documents
Lossless Compression of Structured Convolutional Models via Lifting
Uncertainty in Gradient Boosting via Ensembles
An Unsupervised Deep Learning Approach for Real-World Image Denoising
Conformation-Guided Molecular Representation with Hamiltonian Neural Networks
Neural ODE Processes
Towards Robustness Against Natural Language Word Substitutions
Multi-Class Uncertainty Calibration via Mutual Information Maximization-based Binning
Effective Distributed Learning with Random Features: Improved Bounds and Algorithms
On Learning Universal Representations Across Languages
Minimum Width for Universal Approximation
Factorizing Declarative and Procedural Knowledge in Structured, Dynamical Environments
Regularization Matters in Policy Optimization - An Empirical Study on Continuous Control
Self-Supervised Learning of Compressed Video Representations
Initialization and Regularization of Factorized Neural Layers
Predicting Infectiousness for Proactive Contact Tracing
Are Neural Rankers still Outperformed by Gradient Boosted Decision Trees?
Trusted Multi-View Classification
Anatomy of Catastrophic Forgetting: Hidden Representations and Task Semantics
On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning
Efficient Wasserstein Natural Gradients for Reinforcement Learning
Robust Pruning at Initialization
Parameter Efficient Multimodal Transformers for Video Representation Learning
Active Contrastive Learning of Audio-Visual Video Representations
Enforcing robust control guarantees within neural network policies
Contrastive Divergence Learning is a Time Reversal Adversarial Game
Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding
Domain-Robust Visual Imitation Learning with Mutual Information Constraints
Theoretical bounds on estimation error for meta-learning
Towards Impartial Multi-task Learning
Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data
Counterfactual Generative Networks
IOT: Instance-wise Layer Reordering for Transformer Structures
A statistical theory of cold posteriors in deep neural networks
The inductive bias of ReLU networks on orthogonally separable data
A Unified Approach to Interpreting and Boosting Adversarial Transferability
Contextual Transformation Networks for Online Continual Learning
Private Image Reconstruction from System Side Channels Using Generative Models
GAN "Steerability" without optimization
Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose?
HalentNet: Multimodal Trajectory Forecasting with Hallucinative Intents
Do 2D GANs Know 3D Shape? Unsupervised 3D Shape Reconstruction from 2D Image GANs
AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights
Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures
Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds
Blending MPC & Value Function Approximation for Efficient Reinforcement Learning
Using latent space regression to analyze and leverage compositionality in GANs
Shape-Texture Debiased Neural Network Training
Is Label Smoothing Truly Incompatible with Knowledge Distillation: An Empirical Study
DC3: A learning method for optimization with hard constraints
On the geometry of generalization and memorization in deep neural networks
Exploring the Uncertainty Properties of Neural Networks’ Implicit Priors in the Infinite-Width Limit
Usable Information and Evolution of Optimal Representations During Training
Learning Invariant Representations for Reinforcement Learning without Reconstruction
Parrot: Data-Driven Behavioral Priors for Reinforcement Learning
Zero-Cost Proxies for Lightweight NAS
Perceptual Adversarial Robustness: Defense Against Unseen Threat Models
Deep Neural Network Fingerprinting by Conferrable Adversarial Examples
Enjoy Your Editing: Controllable GANs for Image Editing via Latent Space Navigation
Noise or Signal: The Role of Image Backgrounds in Object Recognition
Shapley explainability on the data manifold
Improving Transformation Invariance in Contrastive Representation Learning
Learning "What-if" Explanations for Sequential Decision-Making
A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention
Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking
Graph Convolution with Low-rank Learnable Local Filters
Meta-Learning with Neural Tangent Kernels
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization
Colorization Transformer
Human-Level Performance in No-Press Diplomacy via Equilibrium Search
Separation and Concentration in Deep Networks
Training GANs with Stronger Augmentations via Contrastive Discriminator
Locally Free Weight Sharing for Network Width Search
Language-Agnostic Representation Learning of Source Code from Structure and Context
Learning Mesh-Based Simulation with Graph Networks
Set Prediction without Imposing Structure as Conditional Density Estimation
Do not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning
Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search
What are the Statistical Limits of Offline RL with Linear Function Approximation?
Learning Accurate Entropy Model with Global Reference for Image Compression
What Makes Instance Discrimination Good for Transfer Learning?
Improving Adversarial Robustness via Channel-wise Activation Suppressing
A unifying view on implicit bias in training linear neural networks
Representation Learning for Sequence Data with Deep Autoencoding Predictive Components
Policy-Driven Attack: Learning to Query for Hard-label Black-box Adversarial Examples
Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate
Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning
Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers
Learning Cross-Domain Correspondence for Control with Dynamics Cycle-Consistency
Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective
Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth
BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration
UPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers
A Good Image Generator Is What You Need for High-Resolution Video Synthesis
What Should Not Be Contrastive in Contrastive Learning
A Design Space Study for LISTA and Beyond
Rethinking Soft Labels for Knowledge Distillation: A Bias–Variance Tradeoff Perspective
Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval
Hierarchical Reinforcement Learning by Discovering Intrinsic Options
Denoising Diffusion Implicit Models
Intraclass clustering: an implicit learning ability that regularizes DNNs
Contrastive Learning with Hard Negative Samples
Discrete Graph Structure Learning for Forecasting Multiple Time Series
Selectivity considered harmful: evaluating the causal impact of class selectivity in DNNs
Watch-And-Help: A Challenge for Social Perception and Human-AI Collaboration
Data-Efficient Reinforcement Learning with Self-Predictive Representations
A Distributional Approach to Controlled Text Generation
A Block Minifloat Representation for Training Deep Neural Networks
On the Impossibility of Global Convergence in Multi-Loss Optimization
Self-supervised Representation Learning with Relative Predictive Coding
Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images
Heating up decision boundaries: isocapacitory saturation, adversarial scenarios and generalization bounds
Rethinking Architecture Selection in Differentiable NAS
CaPC Learning: Confidential and Private Collaborative Learning
Incorporating Symmetry into Deep Dynamics Models for Improved Generalization
Dataset Condensation with Gradient Matching
PMI-Masking: Principled masking of correlated spans
Sharpness-aware Minimization for Efficiently Improving Generalization
Learning with AMIGo: Adversarially Motivated Intrinsic Goals
Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning
End-to-end Adversarial Text-to-Speech
SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness
not-MIWAE: Deep Generative Modelling with Missing not at Random Data
Distilling Knowledge from Reader to Retriever for Question Answering
Adaptive Extra-Gradient Methods for Min-Max Optimization and Games
Training with Quantization Noise for Extreme Model Compression
The Role of Momentum Parameters in the Optimal Convergence of Adaptive Polyak's Heavy-ball Methods
IEPT: Instance-Level and Episode-Level Pretext Tasks for Few-Shot Learning
ChipNet: Budget-Aware Pruning with Heaviside Continuous Approximations
Learning Long-term Visual Dynamics with Region Proposal Interaction Networks
Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity
Conditional Generative Modeling via Learning the Latent Space
When Optimizing $f$-Divergence is Robust with Label Noise
Contrastive Learning with Adversarial Perturbations for Conditional Text Generation
Self-Supervised Policy Adaptation during Deployment
Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks
Large Scale Image Completion via Co-Modulated Generative Adversarial Networks
Geometry-aware Instance-reweighted Adversarial Training
Efficient Reinforcement Learning in Factored MDPs with Application to Constrained RL
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
Bag of Tricks for Adversarial Training
DynaTune: Dynamic Tensor Program Optimization in Deep Neural Network Compilation
The Risks of Invariant Risk Minimization
DOP: Off-Policy Multi-Agent Decomposed Policy Gradients
Generative Time-series Modeling with Fourier Flows
Individually Fair Gradient Boosting
Federated Learning Based on Dynamic Regularization
Contemplating Real-World Object Classification
When Do Curricula Work?
Learning Neural Event Functions for Ordinary Differential Equations
Mastering Atari with Discrete World Models
Getting a CLUE: A Method for Explaining Uncertainty Estimates
Single-Timescale Actor-Critic Provably Finds Globally Optimal Policy
DeLighT: Deep and Light-weight Transformer
Domain Generalization with MixStyle
Concept Learners for Few-Shot Learning
Creative Sketch Generation
Rethinking Embedding Coupling in Pre-trained Language Models
How Does Mixup Help With Robustness and Generalization?
Lifelong Learning of Compositional Structures
Debiasing Concept-based Explanations with Causal Analysis
Learning to Represent Action Values as a Hypergraph on the Action Vertices
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks
Rethinking Attention with Performers
Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods
Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator
Mutual Information State Intrinsic Control
Learning explanations that are hard to vary
Modelling Hierarchical Structure between Dialogue Policy and Natural Language Generator with Option Framework for Task-oriented Dialogue System
Physics-aware, probabilistic model order reduction with guaranteed stability
RODE: Learning Roles to Decompose Multi-Agent Tasks
Neural gradients are near-lognormal: improved quantized and sparse training
Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics
Property Controllable Variational Autoencoder via Invertible Mutual Dependence
Neural Thompson Sampling
Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization
Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization
Effective and Efficient Vote Attack on Capsule Networks
Information Laundering for Model Privacy
Isometric Propagation Network for Generalized Zero-shot Learning
Learning with Feature-Dependent Label Noise: A Progressive Approach
SEED: Self-supervised Distillation For Visual Representation
Unsupervised Audiovisual Synthesis via Exemplar Autoencoders
Learning Energy-Based Generative Models via Coarse-to-Fine Expanding and Sampling
DDPNOpt: Differential Dynamic Programming Neural Optimizer
Mirostat: A Neural Text Decoding Algorithm That Directly Controls Perplexity
Contextual Dropout: An Efficient Sample-Dependent Dropout Module
Proximal Gradient Descent-Ascent: Variable Convergence under KŁ Geometry
Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with $1/n$ Parameters
Large Associative Memory Problem in Neurobiology and Machine Learning
Efficient Transformers in Reinforcement Learning using Actor-Learner Distillation
On the Dynamics of Training Attention Models
INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving
A Critique of Self-Expressive Deep Subspace Clustering
Learning to Recombine and Resample Data For Compositional Generalization
Learning Generalizable Visual Representations via Interactive Gameplay
Overparameterisation and worst-case generalisation: friend or foe?
Calibration tests beyond classification
On the Transfer of Disentangled Representations in Realistic Settings
Deep Neural Tangent Kernel and Laplace Kernel Have the Same RKHS
Revisiting Few-sample BERT Fine-tuning
Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learning
SSD: A Unified Framework for Self-Supervised Outlier Detection
Long-tail learning via logit adjustment
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms
Understanding the role of importance weighting for deep learning
LEAF: A Learnable Frontend for Audio Classification
Monotonic Kronecker-Factored Lattice
Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data
Vulnerability-Aware Poisoning Mechanism for Online RL with Unknown Dynamics
Wasserstein-2 Generative Networks
Emergent Road Rules In Multi-Agent Driving Environments
Iterative Empirical Game Solving via Single Policy Best Response
Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes
Generative Language-Grounded Policy in Vision-and-Language Navigation with Bayes' Rule
Understanding the failure modes of out-of-distribution generalization
Uncertainty Estimation and Calibration with Finite-State Probabilistic RNNs
Hopfield Networks is All You Need
The Importance of Pessimism in Fixed-Dataset Policy Optimization
Representation Balancing Offline Model-based Reinforcement Learning
FairBatch: Batch Selection for Model Fairness
Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks
Systematic generalisation with group invariant predictions
Efficient Inference of Flexible Interaction in Spiking-neuron Networks
Graph Coarsening with Neural Networks
Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks
Are wider nets better given the same number of parameters?
Autoregressive Entity Retrieval
DARTS-: Robustly Stepping out of Performance Collapse Without Indicators
Adversarially Guided Actor-Critic
Balancing Constraints and Rewards with Meta-Gradient D4PG
Auxiliary Learning by Implicit Differentiation
Distributed Momentum for Byzantine-resilient Stochastic Gradient Descent
Large-width functional asymptotics for deep Gaussian neural networks
Deciphering and Optimizing Multi-Task Learning: a Random Matrix Approach
Free Lunch for Few-shot Learning: Distribution Calibration
Generalized Multimodal ELBO
Targeted Attack against Deep Neural Networks via Flipping Limited Weight Bits
Convex Regularization behind Neural Reconstruction
Efficient Certified Defenses Against Patch Attacks on Image Classifiers
Learning Neural Generative Dynamics for Molecular Conformation Generation
Individually Fair Rankings
Hierarchical Autoregressive Modeling for Neural Video Compression
Robust Reinforcement Learning on State Observations with Learned Optimal Adversary
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks?
A Diffusion Theory For Deep Learning Dynamics: Stochastic Gradient Descent Exponentially Favors Flat Minima
Evaluation of Similarity-based Explanations
Geometry-Aware Gradient Algorithms for Neural Architecture Search
Open Question Answering over Tables and Text
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods
VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models
Self-supervised Learning from a Multi-view Perspective
Fair Mixup: Fairness via Interpolation
Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning
Removing Undesirable Feature Contributions Using Out-of-Distribution Data
Meta-learning Symmetries by Reparameterization
How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision
For self-supervised learning, Rationality implies generalization, provably
A Temporal Kernel Approach for Deep Learning with Continuous-time Information
Improve Object Detection with Feature-based Knowledge Distillation: Towards Accurate and Efficient Detectors
Conservative Safety Critics for Exploration
Model-Based Visual Planning with Self-Supervised Functional Distances
GraphCodeBERT: Pre-training Code Representations with Data Flow
No MCMC for me: Amortized sampling for fast and stable training of energy-based models
BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction
Predicting Classification Accuracy When Adding New Unobserved Classes
Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors
Learning the Pareto Front with Hypernetworks
Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime
Projected Latent Markov Chain Monte Carlo: Conditional Sampling of Normalizing Flows
MODALS: Modality-agnostic Automated Data Augmentation in the Latent Space
Impact of Representation Learning in Linear Bandits
EEC: Learning to Encode and Regenerate Images for Continual Learning
What Can You Learn From Your Muscles? Learning Visual Representation from Human Interactions
Improving VAEs' Robustness to Adversarial Attack
The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers
Control-Aware Representations for Model-based Reinforcement Learning
Scaling Symbolic Methods using Gradients for Neural Model Explanation
Empirical or Invariant Risk Minimization? A Sample Complexity Perspective
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning
Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients
Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability
The geometry of integration in text classification RNNs
On the Bottleneck of Graph Neural Networks and its Practical Implications
Learning to Reach Goals via Iterated Supervised Learning
On the Critical Role of Conventions in Adaptive Human-AI Collaboration
CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks
Discovering a set of policies for the worst case reward
Learning perturbation sets for robust machine learning
Primal Wasserstein Imitation Learning
A Universal Representation Transformer Layer for Few-Shot Image Classification
MoPro: Webly Supervised Learning with Momentum Prototypes
Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU
Optimism in Reinforcement Learning with Generalized Linear Function Approximation
Deberta: Decoding-Enhanced Bert With Disentangled Attention
Variational Information Bottleneck for Effective Low-Resource Fine-Tuning
Expressive Power of Invariant and Equivariant Graph Neural Networks
On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines
Computational Separation Between Convolutional and Fully-Connected Networks
Probabilistic Numeric Convolutional Neural Networks
FairFil: Contrastive Neural Debiasing Method for Pretrained Text Encoders
Coping with Label Shift via Distributionally Robust Optimisation
MixKD: Towards Efficient Distillation of Large-scale Language Models
Learning a Latent Simplex in Input Sparsity Time
Teaching with Commentaries
CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding
Fantastic Four: Differentiable and Efficient Bounds on Singular Values of Convolution Layers
Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data
Negative Data Augmentation
Scalable Transfer Learning with Expert Models
A Wigner-Eckart Theorem for Group Equivariant Convolution Kernels
Learning A Minimax Optimizer: A Pilot Study
Meta Back-Translation
Optimal Regularization can Mitigate Double Descent
Net-DNF: Effective Deep Modeling of Tabular Data
MultiModalQA: complex question answering over text, tables and images
Dynamic Tensor Rematerialization
AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models
Few-Shot Learning via Learning the Representation, Provably
Wandering within a world: Online contextualized few-shot learning
WrapNet: Neural Net Inference with Ultra-Low-Precision Arithmetic
Nearest Neighbor Machine Translation
Knowledge distillation via softmax regression representation learning
Empirical Analysis of Unlabeled Entity Problem in Named Entity Recognition
Practical Massively Parallel Monte-Carlo Tree Search Applied to Molecular Design
Neural Pruning via Growing Regularization
Mixed-Features Vectors and Subspace Splitting
Graph-Based Continual Learning
Sparse Quantized Spectral Clustering
Taking Notes on the Fly Helps Language Pre-Training
Explainable Deep One-Class Classification
Revisiting Dynamic Convolution via Matrix Decomposition
BiPointNet: Binary Neural Network for Point Clouds
Prediction and generalisation over directed actions by grid cells
Continual learning in recurrent neural networks
Neural networks with late-phase weights
Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization
HyperGrid Transformers: Towards A Single Model for Multiple Tasks
Learning Robust State Abstractions for Hidden-Parameter Block MDPs
Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-Learning
Viewmaker Networks: Learning Views for Unsupervised Representation Learning
Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning
Is Attention Better Than Matrix Decomposition?
Learning Incompressible Fluid Dynamics from Scratch - Towards Fast, Differentiable Fluid Models that Generalize
Refining Deep Generative Models via Discriminator Gradient Flow
Entropic gradient descent algorithms and wide flat minima
New Bounds For Distributed Mean Estimation and Variance Reduction
Learning Value Functions in Deep Policy Gradients using Residual Variance
Winning the L2RPN Challenge: Power Grid Management via Semi-Markov Afterstate Actor-Critic
No Cost Likelihood Manipulation at Test Time for Making Better Mistakes in Deep Networks
Parameter-Based Value Functions
CoCon: A Self-Supervised Approach for Controlled Text Generation
MELR: Meta-Learning via Modeling Episode-Level Relationships for Few-Shot Learning
Beyond Categorical Label Representations for Image Classification
Meta-learning with negative learning rates
Provable Rich Observation Reinforcement Learning with Combinatorial Latent States
Global optimality of softmax policy gradient with single hidden layer neural networks in the mean-field regime
Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF
Contrastive Syn-to-Real Generalization
Evolving Reinforcement Learning Algorithms
Neural Topic Model via Optimal Transport
Class Normalization for (Continual)? Generalized Zero-Shot Learning
Revisiting Hierarchical Approach for Persistent Long-Term Video Prediction
A Gradient Flow Framework For Analyzing Network Pruning
A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes
Knowledge Distillation as Semiparametric Inference
NBDT: Neural-Backed Decision Tree
Deep Equals Shallow for ReLU Networks in Kernel Regimes
Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling
MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering
Mind the Pad -- CNNs Can Develop Blind Spots
A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks
Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs
Interpretable Models for Granger Causality Using Self-explaining Neural Networks
Estimating Lipschitz constants of monotone deep equilibrium models
Probing BERT in Hyperbolic Spaces
Batch Reinforcement Learning Through Continuation Method
Towards Resolving the Implicit Bias of Gradient Descent for Matrix Factorization: Greedy Low-Rank Learning
Diverse Video Generation using a Gaussian Process Trigger
Learning and Evaluating Representations for Deep One-Class Classification
Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers
Retrieval-Augmented Generation for Code Summarization via Hybrid GNN
Randomized Automatic Differentiation
Generalized Energy Based Models
Temporally-Extended ε-Greedy Exploration
Multiplicative Filter Networks
Clustering-friendly Representation Learning via Instance Discrimination and Feature Decorrelation
FedMix: Approximation of Mixup under Mean Augmented Federated Learning
Self-training For Few-shot Transfer Across Extreme Task Differences
VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments
Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks
Generalization bounds via distillation
Model Patching: Closing the Subgroup Performance Gap with Data Augmentation
A Learning Theoretic Perspective on Local Explainability
Sharper Generalization Bounds for Learning with Gradient-dominated Objective Functions
Learning to Set Waypoints for Audio-Visual Navigation
Partitioned Learned Bloom Filters
Unsupervised Meta-Learning through Latent-Space Interpolation in Generative Models
Generalized Variational Continual Learning
Robust and Generalizable Visual Representation Learning via Random Convolutions
One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks
X2T: Training an X-to-Text Typing Interface with Online Learning from User Feedback
Attentional Constellation Nets for Few-Shot Learning
InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective
Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech
Risk-Averse Offline Reinforcement Learning
Spatio-Temporal Graph Scattering Transform
How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks
Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priors
Communication in Multi-Agent Reinforcement Learning: Intention Sharing
Few-Shot Bayesian Optimization with Deep Kernel Surrogates
Disentangled Recurrent Wasserstein Autoencoder
In Search of Lost Domain Generalization
Cross-Attentional Audio-Visual Fusion for Weakly-Supervised Action Localization
Implicit Normalizing Flows
VTNet: Visual Transformer Network for Object Goal Navigation
Learning Task Decomposition with Ordered Memory Policy Network
Deep Networks and the Multiple Manifold Problem
Learning What To Do by Simulating the Past
Progressive Skeletonization: Trimming more fat from a network at initialization
$i$-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning
Topology-Aware Segmentation Using Discrete Morse Theory
On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections
Tent: Fully Test-Time Adaptation by Entropy Minimization
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding
Dataset Inference: Ownership Resolution in Machine Learning
Regularized Inverse Reinforcement Learning
Fast And Slow Learning Of Recurrent Independent Mechanisms
Learning Safe Multi-agent Control with Decentralized Neural Barrier Certificates
Semi-supervised Keypoint Localization
Representing Partial Programs with Blended Abstract Semantics
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
Unlearnable Examples: Making Personal Data Unexploitable
IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression
Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomization
Identifying Physical Law of Hamiltonian Systems via Meta-Learning
Text Generation by Learning from Demonstrations
Unbiased Teacher for Semi-Supervised Object Detection
Estimating informativeness of samples with Smooth Unique Information
Efficient Generalized Spherical CNNs
DialoGraph: Incorporating Interpretable Strategy-Graph Networks into Negotiation Dialogues
Global Convergence of Three-layer Neural Networks in the Mean Field Regime
Multi-Time Attention Networks for Irregularly Sampled Time Series
Linear Convergent Decentralized Optimization with Compression
Adaptive Federated Optimization
Auction Learning as a Two-Player Game
Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective
Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization
Interpreting Knowledge Graph Relation Representation from Word Embeddings
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction
DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation
Learning-based Support Estimation in Sublinear Time
Integrating Categorical Semantics into Unsupervised Domain Translation
The Recurrent Neural Tangent Kernel
C-Learning: Learning to Achieve Goals via Recursive Classification
Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning
What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study
Influence Estimation for Generative Adversarial Networks
Clairvoyance: A Pipeline Toolkit for Medical Time Series
Randomized Ensembled Double Q-Learning: Learning Fast Without a Model
Learning advanced mathematical computations from examples
Noise against noise: stochastic label noise helps combat inherent label noise
Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning
Protecting DNNs from Theft using an Ensemble of Diverse Models
You Only Need Adversarial Supervision for Semantic Image Synthesis
Neural Spatio-Temporal Point Processes
Linear Mode Connectivity in Multitask and Continual Learning
Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders
Auxiliary Task Update Decomposition: The Good, the Bad and the Neutral
Influence Functions in Deep Learning Are Fragile
Categorical Normalizing Flows via Continuous Transformations
Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation
Directed Acyclic Graph Neural Networks
Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models
SOLAR: Sparse Orthogonal Learned and Random Embeddings
Bayesian Context Aggregation for Neural Processes
Cut out the annotator, keep the cutout: better segmentation with weak supervision
Effective Abstract Reasoning with Dual-Contrast Network
Personalized Federated Learning with First Order Model Optimization
Task-Agnostic Morphology Evolution
Learning Parametrised Graph Shift Operators
Online Adversarial Purification based on Self-supervised Learning
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