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Distributionally Robust Fair Principal Components via Geodesic Descents
Ancestral protein sequence reconstruction using a tree-structured Ornstein-Uhlenbeck variational autoencoder
The Efficiency Misnomer
The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks
Huber Additive Models for Non-stationary Time Series Analysis
Rethinking Supervised Pre-Training for Better Downstream Transferring
Optimization inspired Multi-Branch Equilibrium Models
Know Your Action Set: Learning Action Relations for Reinforcement Learning
On the Importance of Difficulty Calibration in Membership Inference Attacks
Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset
Rethinking Class-Prior Estimation for Positive-Unlabeled Learning
Enabling Arbitrary Translation Objectives with Adaptive Tree Search
Fine-grained Differentiable Physics: A Yarn-level Model for Fabrics
Transition to Linearity of Wide Neural Networks is an Emerging Property of Assembling Weak Models
SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation
Is Importance Weighting Incompatible with Interpolating Classifiers?
DIVA: Dataset Derivative of a Learning Task
Path Integral Sampler: A Stochastic Control Approach For Sampling
Feature Kernel Distillation
Representation Learning for Online and Offline RL in Low-rank MDPs
Tackling the Generative Learning Trilemma with Denoising Diffusion GANs
Strength of Minibatch Noise in SGD
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels
Online Facility Location with Predictions
High Probability Bounds for a Class of Nonconvex Algorithms with AdaGrad Stepsize
Contrastive Fine-grained Class Clustering via Generative Adversarial Networks
TAMP-S2GCNets: Coupling Time-Aware Multipersistence Knowledge Representation with Spatio-Supra Graph Convolutional Networks for Time-Series Forecasting
D-CODE: Discovering Closed-form ODEs from Observed Trajectories
Declarative nets that are equilibrium models
Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks
DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR
Graph-Relational Domain Adaptation
On the approximation properties of recurrent encoder-decoder architectures
Learning Object-Oriented Dynamics for Planning from Text
Quantitative Performance Assessment of CNN Units via Topological Entropy Calculation
The Hidden Convex Optimization Landscape of Regularized Two-Layer ReLU Networks: an Exact Characterization of Optimal Solutions
AlphaZero-based Proof Cost Network to Aid Game Solving
Open-World Semi-Supervised Learning
A generalization of the randomized singular value decomposition
Learning Graphon Mean Field Games and Approximate Nash Equilibria
A Neural Tangent Kernel Perspective of Infinite Tree Ensembles
Multitask Prompted Training Enables Zero-Shot Task Generalization
On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning
On the benefits of maximum likelihood estimation for Regression and Forecasting
Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods
Universalizing Weak Supervision
Uncertainty Modeling for Out-of-Distribution Generalization
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness
Neural Link Prediction with Walk Pooling
On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning
When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently?
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models
Crystal Diffusion Variational Autoencoder for Periodic Material Generation
High Probability Generalization Bounds with Fast Rates for Minimax Problems
Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking
Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations
Multimeasurement Generative Models
Generalized Natural Gradient Flows in Hidden Convex-Concave Games and GANs
Solving Inverse Problems in Medical Imaging with Score-Based Generative Models
switch-GLAT: Multilingual Parallel Machine Translation Via Code-Switch Decoder
MAML is a Noisy Contrastive Learner in Classification
A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion
Implicit Bias of Adversarial Training for Deep Neural Networks
Partial Wasserstein Adversarial Network for Non-rigid Point Set Registration
POETREE: Interpretable Policy Learning with Adaptive Decision Trees
A Johnson-Lindenstrauss Framework for Randomly Initialized CNNs
Online Ad Hoc Teamwork under Partial Observability
Global Convergence of Multi-Agent Policy Gradient in Markov Potential Games
Neural Networks as Kernel Learners: The Silent Alignment Effect
A Fine-Grained Analysis on Distribution Shift
The Inductive Bias of In-Context Learning: Rethinking Pretraining Example Design
Towards Evaluating the Robustness of Neural Networks Learned by Transduction
GradSign: Model Performance Inference with Theoretical Insights
Consistent Counterfactuals for Deep Models
The Three Stages of Learning Dynamics in High-dimensional Kernel Methods
Real-Time Neural Voice Camouflage
Sample Efficient Stochastic Policy Extragradient Algorithm for Zero-Sum Markov Game
Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100
The Effects of Invertibility on the Representational Complexity of Encoders in Variational Autoencoders
Extending the WILDS Benchmark for Unsupervised Adaptation
Efficiently Modeling Long Sequences with Structured State Spaces
Unified Visual Transformer Compression
NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy
Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting
A Theory of Tournament Representations
Mapping conditional distributions for domain adaptation under generalized target shift
NeuPL: Neural Population Learning
An Operator Theoretic View On Pruning Deep Neural Networks
MetaMorph: Learning Universal Controllers with Transformers
PER-ETD: A Polynomially Efficient Emphatic Temporal Difference Learning Method
Modular Lifelong Reinforcement Learning via Neural Composition
TPU-GAN: Learning temporal coherence from dynamic point cloud sequences
Learning Hierarchical Structures with Differentiable Nondeterministic Stacks
Missingness Bias in Model Debugging
Accelerated Policy Learning with Parallel Differentiable Simulation
Causal Contextual Bandits with Targeted Interventions
Learning Altruistic Behaviours in Reinforcement Learning without External Rewards
FILIP: Fine-grained Interactive Language-Image Pre-Training
Sample Efficient Deep Reinforcement Learning via Uncertainty Estimation
The Convex Geometry of Backpropagation: Neural Network Gradient Flows Converge to Extreme Points of the Dual Convex Program
Variational oracle guiding for reinforcement learning
P-Adapters: Robustly Extracting Factual Information from Language Models with Diverse Prompts
Demystifying Batch Normalization in ReLU Networks: Equivalent Convex Optimization Models and Implicit Regularization
Cross-Lingual Transfer with Class-Weighted Language-Invariant Representations
Reverse Engineering of Imperceptible Adversarial Image Perturbations
Pareto Policy Adaptation
Variational autoencoders in the presence of low-dimensional data: landscape and implicit bias
Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design
C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks
Understanding Domain Randomization for Sim-to-real Transfer
Model-Based Offline Meta-Reinforcement Learning with Regularization
Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation
Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface
TRGP: Trust Region Gradient Projection for Continual Learning
Contextualized Scene Imagination for Generative Commonsense Reasoning
Shallow and Deep Networks are Near-Optimal Approximators of Korobov Functions
Phenomenology of Double Descent in Finite-Width Neural Networks
InfinityGAN: Towards Infinite-Pixel Image Synthesis
Discovering Nonlinear PDEs from Scarce Data with Physics-encoded Learning
COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks
How Much Can CLIP Benefit Vision-and-Language Tasks?
Learning Causal Models from Conditional Moment Restrictions by Importance Weighting
Learning Optimal Conformal Classifiers
Self-Supervision Enhanced Feature Selection with Correlated Gates
Understanding the Variance Collapse of SVGD in High Dimensions
Information Bottleneck: Exact Analysis of (Quantized) Neural Networks
CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting
OBJECT DYNAMICS DISTILLATION FOR SCENE DECOMPOSITION AND REPRESENTATION
$\beta$-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap
Augmented Sliced Wasserstein Distances
Neural Processes with Stochastic Attention: Paying more attention to the context dataset
Learnability of convolutional neural networks for infinite dimensional input via mixed and anisotropic smoothness
Towards General Function Approximation in Zero-Sum Markov Games
Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning
Counterfactual Plans under Distributional Ambiguity
Boosted Curriculum Reinforcement Learning
TAda! Temporally-Adaptive Convolutions for Video Understanding
Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations
$\mathrm{SO}(2)$-Equivariant Reinforcement Learning
Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference
Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How
Equivariant Graph Mechanics Networks with Constraints
THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling
SPIRAL: Self-supervised Perturbation-Invariant Representation Learning for Speech Pre-Training
Understanding Dimensional Collapse in Contrastive Self-supervised Learning
Constrained Physical-Statistics Models for Dynamical System Identification and Prediction
Anti-Concentrated Confidence Bonuses For Scalable Exploration
Learning Distributionally Robust Models at Scale via Composite Optimization
On Predicting Generalization using GANs
Offline Neural Contextual Bandits: Pessimism, Optimization and Generalization
Deep Point Cloud Reconstruction
Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series
Half-Inverse Gradients for Physical Deep Learning
Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?
On the relation between statistical learning and perceptual distances
Graph-based Nearest Neighbor Search in Hyperbolic Spaces
Generalized rectifier wavelet covariance models for texture synthesis
Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations
PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series
Variational Neural Cellular Automata
On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks
Generalization Through the Lens of Leave-One-Out Error
Variational methods for simulation-based inference
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions
Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning
Practical Conditional Neural Process Via Tractable Dependent Predictions
CodeTrek: Flexible Modeling of Code using an Extensible Relational Representation
Selective Ensembles for Consistent Predictions
Multi-Stage Episodic Control for Strategic Exploration in Text Games
Surreal-GAN:Semi-Supervised Representation Learning via GAN for uncovering heterogeneous disease-related imaging patterns
A First-Occupancy Representation for Reinforcement Learning
Space-Time Graph Neural Networks
Offline Reinforcement Learning with Value-based Episodic Memory
The Boltzmann Policy Distribution: Accounting for Systematic Suboptimality in Human Models
Defending Against Image Corruptions Through Adversarial Augmentations
Object Pursuit: Building a Space of Objects via Discriminative Weight Generation
The Spectral Bias of Polynomial Neural Networks
Synchromesh: Reliable Code Generation from Pre-trained Language Models
Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction
Fast topological clustering with Wasserstein distance
Skill-based Meta-Reinforcement Learning
Differentiable Scaffolding Tree for Molecule Optimization
Revisiting Design Choices in Offline Model Based Reinforcement Learning
An Experimental Design Perspective on Model-Based Reinforcement Learning
The Evolution of Uncertainty of Learning in Games
CoMPS: Continual Meta Policy Search
Reinforcement Learning in Presence of Discrete Markovian Context Evolution
Provable Learning-based Algorithm For Sparse Recovery
Granger causal inference on DAGs identifies genomic loci regulating transcription
Curriculum learning as a tool to uncover learning principles in the brain
Learning Temporally Causal Latent Processes from General Temporal Data
Autoregressive Quantile Flows for Predictive Uncertainty Estimation
Bi-linear Value Networks for Multi-goal Reinforcement Learning
Deep ReLU Networks Preserve Expected Length
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap
HyperDQN: A Randomized Exploration Method for Deep Reinforcement Learning
What Do We Mean by Generalization in Federated Learning?
A Comparison of Hamming Errors of Representative Variable Selection Methods
Model Zoo: A Growing Brain That Learns Continually
Distributional Reinforcement Learning with Monotonic Splines
Anisotropic Random Feature Regression in High Dimensions
Modeling Label Space Interactions in Multi-label Classification using Box Embeddings
Variational Predictive Routing with Nested Subjective Timescales
Training invariances and the low-rank phenomenon: beyond linear networks
A NON-PARAMETRIC REGRESSION VIEWPOINT : GENERALIZATION OF OVERPARAMETRIZED DEEP RELU NETWORK UNDER NOISY OBSERVATIONS
Top-N: Equivariant Set and Graph Generation without Exchangeability
LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5
How Does SimSiam Avoid Collapse Without Negative Samples? A Unified Understanding with Self-supervised Contrastive Learning
FedChain: Chained Algorithms for Near-optimal Communication Cost in Federated Learning
Eigencurve: Optimal Learning Rate Schedule for SGD on Quadratic Objectives with Skewed Hessian Spectrums
Reinforcement Learning under a Multi-agent Predictive State Representation Model: Method and Theory
Unsupervised Learning of Full-Waveform Inversion: Connecting CNN and Partial Differential Equation in a Loop
Representational Continuity for Unsupervised Continual Learning
Context-Aware Sparse Deep Coordination Graphs
The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models
Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models
Towards Understanding Generalization via Decomposing Excess Risk Dynamics
Chunked Autoregressive GAN for Conditional Waveform Synthesis
Topological Experience Replay
Generalisation in Lifelong Reinforcement Learning through Logical Composition
Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System
Discovering Invariant Rationales for Graph Neural Networks
DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools
Encoding Weights of Irregular Sparsity for Fixed-to-Fixed Model Compression
Normalization of Language Embeddings for Cross-Lingual Alignment
Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality
Do We Need Anisotropic Graph Neural Networks?
Learning to Guide and to be Guided in the Architect-Builder Problem
Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems
Towards a Unified View of Parameter-Efficient Transfer Learning
Few-Shot Backdoor Attacks on Visual Object Tracking
Relational Learning with Variational Bayes
Scattering Networks on the Sphere for Scalable and Rotationally Equivariant Spherical CNNs
GATSBI: Generative Adversarial Training for Simulation-Based Inference
Multiset-Equivariant Set Prediction with Approximate Implicit Differentiation
Increasing the Cost of Model Extraction with Calibrated Proof of Work
BAM: Bayes with Adaptive Memory
Open-Set Recognition: A Good Closed-Set Classifier is All You Need
Is High Variance Unavoidable in RL? A Case Study in Continuous Control
Predicting Physics in Mesh-reduced Space with Temporal Attention
Constructing a Good Behavior Basis for Transfer using Generalized Policy Updates
Lipschitz-constrained Unsupervised Skill Discovery
Task Relatedness-Based Generalization Bounds for Meta Learning
Reinforcement Learning with Sparse Rewards using Guidance from Offline Demonstration
Universal Approximation Under Constraints is Possible with Transformers
Unraveling Model-Agnostic Meta-Learning via The Adaptation Learning Rate
LORD: Lower-Dimensional Embedding of Log-Signature in Neural Rough Differential Equations
Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters
On the Learning and Learnability of Quasimetrics
Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization
Disentanglement Analysis with Partial Information Decomposition
Emergent Communication at Scale
Associated Learning: an Alternative to End-to-End Backpropagation that Works on CNN, RNN, and Transformer
Optimal Transport for Long-Tailed Recognition with Learnable Cost Matrix
Adversarially Robust Conformal Prediction
Generative Pseudo-Inverse Memory
Improving the Accuracy of Learning Example Weights for Imbalance Classification
Learning meta-features for AutoML
Denoising Likelihood Score Matching for Conditional Score-based Data Generation
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data
Neural Markov Controlled SDE: Stochastic Optimization for Continuous-Time Data
A New Perspective on "How Graph Neural Networks Go Beyond Weisfeiler-Lehman?"
Representation-Agnostic Shape Fields
MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts
Looking Back on Learned Experiences For Class/task Incremental Learning
Audio Lottery: Speech Recognition Made Ultra-Lightweight, Noise-Robust, and Transferable
Acceleration of Federated Learning with Alleviated Forgetting in Local Training
Delaunay Component Analysis for Evaluation of Data Representations
Equivariant Transformers for Neural Network based Molecular Potentials
Long Expressive Memory for Sequence Modeling
Training Transition Policies via Distribution Matching for Complex Tasks
Spherical Message Passing for 3D Molecular Graphs
Wisdom of Committees: An Overlooked Approach To Faster and More Accurate Models
Eliminating Sharp Minima from SGD with Truncated Heavy-tailed Noise
Large-Scale Representation Learning on Graphs via Bootstrapping
VAE Approximation Error: ELBO and Exponential Families
Label Leakage and Protection in Two-party Split Learning
Online Adversarial Attacks
Discrepancy-Based Active Learning for Domain Adaptation
Bootstrapping Semantic Segmentation with Regional Contrast
Gradient Matching for Domain Generalization
Open-vocabulary Object Detection via Vision and Language Knowledge Distillation
DISSECT: Disentangled Simultaneous Explanations via Concept Traversals
Exploring Memorization in Adversarial Training
When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations
NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning
Cross-Trajectory Representation Learning for Zero-Shot Generalization in RL
Incremental False Negative Detection for Contrastive Learning
Learning Curves for SGD on Structured Features
AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation
Generative Models as a Data Source for Multiview Representation Learning
Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields
Reliable Adversarial Distillation with Unreliable Teachers
Automated Self-Supervised Learning for Graphs
Stein Latent Optimization for Generative Adversarial Networks
Is Homophily a Necessity for Graph Neural Networks?
Boosting Randomized Smoothing with Variance Reduced Classifiers
Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANs
Query Embedding on Hyper-Relational Knowledge Graphs
Steerable Partial Differential Operators for Equivariant Neural Networks
Learning Multimodal VAEs through Mutual Supervision
Charformer: Fast Character Transformers via Gradient-based Subword Tokenization
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity
The MultiBERTs: BERT Reproductions for Robustness Analysis
ViTGAN: Training GANs with Vision Transformers
Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutions
TAPEX: Table Pre-training via Learning a Neural SQL Executor
Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning
CycleMLP: A MLP-like Architecture for Dense Prediction
Heteroscedastic Temporal Variational Autoencoder For Irregularly Sampled Time Series
Perceiver IO: A General Architecture for Structured Inputs & Outputs
SphereFace2: Binary Classification is All You Need for Deep Face Recognition
Policy Gradients Incorporating the Future
SimVLM: Simple Visual Language Model Pretraining with Weak Supervision
Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners
NASI: Label- and Data-agnostic Neural Architecture Search at Initialization
How to Inject Backdoors with Better Consistency: Logit Anchoring on Clean Data
Divisive Feature Normalization Improves Image Recognition Performance in AlexNet
Finetuned Language Models are Zero-Shot Learners
CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation
Should We Be Pre-training? An Argument for End-task Aware Training as an Alternative
Hindsight Foresight Relabeling for Meta-Reinforcement Learning
Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning
Learning to Downsample for Segmentation of Ultra-High Resolution Images
Vitruvion: A Generative Model of Parametric CAD Sketches
IGLU: Efficient GCN Training via Lazy Updates
Random matrices in service of ML footprint: ternary random features with no performance loss
Geometric and Physical Quantities improve E(3) Equivariant Message Passing
PoNet: Pooling Network for Efficient Token Mixing in Long Sequences
Focus on the Common Good: Group Distributional Robustness Follows
Task Affinity with Maximum Bipartite Matching in Few-Shot Learning
8-bit Optimizers via Block-wise Quantization
Graphon based Clustering and Testing of Networks: Algorithms and Theory
The Information Geometry of Unsupervised Reinforcement Learning
VC dimension of partially quantized neural networks in the overparametrized regime
EntQA: Entity Linking as Question Answering
Evaluating Model-Based Planning and Planner Amortization for Continuous Control
GNN is a Counter? Revisiting GNN for Question Answering
Gradient Step Denoiser for convergent Plug-and-Play
Planning in Stochastic Environments with a Learned Model
Creating Training Sets via Weak Indirect Supervision
Frame Averaging for Invariant and Equivariant Network Design
Taming Sparsely Activated Transformer with Stochastic Experts
From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness
On Non-Random Missing Labels in Semi-Supervised Learning
PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication
QUERY EFFICIENT DECISION BASED SPARSE ATTACKS AGAINST BLACK-BOX DEEP LEARNING MODELS
Which Shortcut Cues Will DNNs Choose? A Study from the Parameter-Space Perspective
EViT: Expediting Vision Transformers via Token Reorganizations
Understanding Intrinsic Robustness Using Label Uncertainty
Label Encoding for Regression Networks
Knowledge Infused Decoding
Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers
Provably Robust Adversarial Examples
A Tale of Two Flows: Cooperative Learning of Langevin Flow and Normalizing Flow Toward Energy-Based Model
Sparsity Winning Twice: Better Robust Generalization from More Efficient Training
Measuring the Interpretability of Unsupervised Representations via Quantized Reversed Probing
Conditional Image Generation by Conditioning Variational Auto-Encoders
Post hoc Explanations may be Ineffective for Detecting Unknown Spurious Correlation
Generalized Decision Transformer for Offline Hindsight Information Matching
SUMNAS: Supernet with Unbiased Meta-Features for Neural Architecture Search
CrossMatch: Cross-Classifier Consistency Regularization for Open-Set Single Domain Generalization
Imitation Learning from Observations under Transition Model Disparity
LOSSY COMPRESSION WITH DISTRIBUTION SHIFT AS ENTROPY CONSTRAINED OPTIMAL TRANSPORT
How Did the Model Change? Efficiently Assessing Machine Learning API Shifts
Semi-relaxed Gromov-Wasserstein divergence and applications on graphs
On Bridging Generic and Personalized Federated Learning for Image Classification
Few-shot Learning via Dirichlet Tessellation Ensemble
Learning to Dequantise with Truncated Flows
Latent Image Animator: Learning to Animate Images via Latent Space Navigation
FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relations
Superclass-Conditional Gaussian Mixture Model For Learning Fine-Grained Embeddings
Unsupervised Vision-Language Grammar Induction with Shared Structure Modeling
Environment Predictive Coding for Visual Navigation
An Agnostic Approach to Federated Learning with Class Imbalance
Vision-Based Manipulators Need to Also See from Their Hands
A Program to Build E(N)-Equivariant Steerable CNNs
Deconstructing the Inductive Biases of Hamiltonian Neural Networks
A Zest of LIME: Towards Architecture-Independent Model Distances
W-CTC: a Connectionist Temporal Classification Loss with Wild Cards
DictFormer: Tiny Transformer with Shared Dictionary
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
RelViT: Concept-guided Vision Transformer for Visual Relational Reasoning
Equivariant Self-Supervised Learning: Encouraging Equivariance in Representations
iLQR-VAE : control-based learning of input-driven dynamics with applications to neural data
Information Gain Propagation: a New Way to Graph Active Learning with Soft Labels
Efficient Token Mixing for Transformers via Adaptive Fourier Neural Operators
Compositional Training for End-to-End Deep AUC Maximization
FedBABU: Toward Enhanced Representation for Federated Image Classification
Unsupervised Semantic Segmentation by Distilling Feature Correspondences
Efficient and Differentiable Conformal Prediction with General Function Classes
Pseudo Numerical Methods for Diffusion Models on Manifolds
Understanding Latent Correlation-Based Multiview Learning and Self-Supervision: An Identifiability Perspective
Learning Weakly-supervised Contrastive Representations
Fast Model Editing at Scale
DEGREE: Decomposition Based Explanation for Graph Neural Networks
Generalizing Few-Shot NAS with Gradient Matching
Sound Adversarial Audio-Visual Navigation
The Neural Data Router: Adaptive Control Flow in Transformers Improves Systematic Generalization
Illiterate DALL-E Learns to Compose
Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators
Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception
Linking Emergent and Natural Languages via Corpus Transfer
Know Thyself: Transferable Visual Control Policies Through Robot-Awareness
PiCO: Contrastive Label Disambiguation for Partial Label Learning
ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity
Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift
Model-augmented Prioritized Experience Replay
Bridging Recommendation and Marketing via Recurrent Intensity Modeling
Continual Learning with Filter Atom Swapping
One After Another: Learning Incremental Skills for a Changing World
DemoDICE: Offline Imitation Learning with Supplementary Imperfect Demonstrations
Learning to Schedule Learning rate with Graph Neural Networks
How Do Vision Transformers Work?
BDDM: Bilateral Denoising Diffusion Models for Fast and High-Quality Speech Synthesis
Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?
On Incorporating Inductive Biases into VAEs
Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral Similarities
Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework
MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMC
Orchestrated Value Mapping for Reinforcement Learning
Gradient Information Matters in Policy Optimization by Back-propagating through Model
Robust Unlearnable Examples: Protecting Data Privacy Against Adversarial Learning
Evaluating Distributional Distortion in Neural Language Modeling
Mapping Language Models to Grounded Conceptual Spaces
UniFormer: Unified Transformer for Efficient Spatial-Temporal Representation Learning
On Robust Prefix-Tuning for Text Classification
Regularized Autoencoders for Isometric Representation Learning
Learning to Generalize across Domains on Single Test Samples
Relational Multi-Task Learning: Modeling Relations between Data and Tasks
ConFeSS: A Framework for Single Source Cross-Domain Few-Shot Learning
GeneDisco: A Benchmark for Experimental Design in Drug Discovery
Scene Transformer: A unified architecture for predicting future trajectories of multiple agents
Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond
How Well Does Self-Supervised Pre-Training Perform with Streaming Data?
Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning
Group-based Interleaved Pipeline Parallelism for Large-scale DNN Training
Practical Integration via Separable Bijective Networks
Switch to Generalize: Domain-Switch Learning for Cross-Domain Few-Shot Classification
Local Feature Swapping for Generalization in Reinforcement Learning
Language modeling via stochastic processes
Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution
Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks
F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization
Neural Relational Inference with Node-Specific Information
DARA: Dynamics-Aware Reward Augmentation in Offline Reinforcement Learning
Training Data Generating Networks: Shape Reconstruction via Bi-level Optimization
GraphENS: Neighbor-Aware Ego Network Synthesis for Class-Imbalanced Node Classification
Retriever: Learning Content-Style Representation as a Token-Level Bipartite Graph
Using Graph Representation Learning with Schema Encoders to Measure the Severity of Depressive Symptoms
Hot-Refresh Model Upgrades with Regression-Free Compatible Training in Image Retrieval
Graph Auto-Encoder via Neighborhood Wasserstein Reconstruction
Neural Deep Equilibrium Solvers
Relational Surrogate Loss Learning
Graph-Enhanced Exploration for Goal-oriented Reinforcement Learning
Conditioning Sequence-to-sequence Networks with Learned Activations
Inductive Relation Prediction Using Analogy Subgraph Embeddings
Continual Normalization: Rethinking Batch Normalization for Online Continual Learning
Divergence-aware Federated Self-Supervised Learning
Controlling the Complexity and Lipschitz Constant improves Polynomial Nets
Generative Principal Component Analysis
Learning Towards The Largest Margins
Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design
Domain Adversarial Training: A Game Perspective
On the Uncomputability of Partition Functions in Energy-Based Sequence Models
Towards Model Agnostic Federated Learning Using Knowledge Distillation
Minimax Optimization with Smooth Algorithmic Adversaries
A Loss Curvature Perspective on Training Instabilities of Deep Learning Models
Backdoor Defense via Decoupling the Training Process
Learnability Lock: Authorized Learnability Control Through Adversarial Invertible Transformations
On Redundancy and Diversity in Cell-based Neural Architecture Search
Coordination Among Neural Modules Through a Shared Global Workspace
RotoGrad: Gradient Homogenization in Multitask Learning
cosFormer: Rethinking Softmax In Attention
Exploring the Limits of Large Scale Pre-training
Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning
KL Guided Domain Adaptation
ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mind
Map Induction: Compositional spatial submap learning for efficient exploration in novel environments
Proving the Lottery Ticket Hypothesis for Convolutional Neural Networks
Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions
Meta-Learning with Fewer Tasks through Task Interpolation
Transformer-based Transform Coding
BEiT: BERT Pre-Training of Image Transformers
Zero Pixel Directional Boundary by Vector Transform
Gaussian Mixture Convolution Networks
Image BERT Pre-training with Online Tokenizer
Online Coreset Selection for Rehearsal-based Continual Learning
Non-Linear Operator Approximations for Initial Value Problems
Better Supervisory Signals by Observing Learning Paths
Bag of Instances Aggregation Boosts Self-supervised Distillation
Omni-Dimensional Dynamic Convolution
Learning State Representations via Retracing in Reinforcement Learning
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training
Learning to Map for Active Semantic Goal Navigation
Transfer RL across Observation Feature Spaces via Model-Based Regularization
CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing
Adversarial Support Alignment
Boosting the Certified Robustness of L-infinity Distance Nets
Spanning Tree-based Graph Generation for Molecules
Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Bound
Policy improvement by planning with Gumbel
Learning Scenario Representation for Solving Two-stage Stochastic Integer Programs
Measuring CLEVRness: Black-box Testing of Visual Reasoning Models
Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation
Subspace Regularizers for Few-Shot Class Incremental Learning
Structure-Aware Transformer Policy for Inhomogeneous Multi-Task Reinforcement Learning
Learning Super-Features for Image Retrieval
Auto-scaling Vision Transformers without Training
Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond
NASViT: Neural Architecture Search for Efficient Vision Transformers with Gradient Conflict aware Supernet Training
L0-Sparse Canonical Correlation Analysis
Exploiting Class Activation Value for Partial-Label Learning
Dual Lottery Ticket Hypothesis
Visual Representation Learning over Latent Domains
Towards Building A Group-based Unsupervised Representation Disentanglement Framework
Benchmarking the Spectrum of Agent Capabilities
Self-ensemble Adversarial Training for Improved Robustness
Learning Prototype-oriented Set Representations for Meta-Learning
Neural Program Synthesis with Query
Peek-a-Boo: What (More) is Disguised in a Randomly Weighted Neural Network, and How to Find It Efficiently
Coherence-based Label Propagation over Time Series for Accelerated Active Learning
Scaling Laws for Neural Machine Translation
Interacting Contour Stochastic Gradient Langevin Dynamics
GDA-AM: ON THE EFFECTIVENESS OF SOLVING MIN-IMAX OPTIMIZATION VIA ANDERSON MIXING
Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs
Evolutionary Diversity Optimization with Clustering-based Selection for Reinforcement Learning
Adversarial Robustness Through the Lens of Causality
Stochastic Training is Not Necessary for Generalization
Conditional Object-Centric Learning from Video
Distributionally Robust Models with Parametric Likelihood Ratios
Connectome-constrained Latent Variable Model of Whole-Brain Neural Activity
Implicit Bias of MSE Gradient Optimization in Underparameterized Neural Networks
Igeood: An Information Geometry Approach to Out-of-Distribution Detection
The Rich Get Richer: Disparate Impact of Semi-Supervised Learning
GiraffeDet: A Heavy-Neck Paradigm for Object Detection
Generative Planning for Temporally Coordinated Exploration in Reinforcement Learning
Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning
$\pi$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization
R4D: Utilizing Reference Objects for Long-Range Distance Estimation
RegionViT: Regional-to-Local Attention for Vision Transformers
Efficient Computation of Deep Nonlinear Infinite-Width Neural Networks that Learn Features
Efficient Active Search for Combinatorial Optimization Problems
No One Representation to Rule Them All: Overlapping Features of Training Methods
Particle Stochastic Dual Coordinate Ascent: Exponential convergent algorithm for mean field neural network optimization
Deep Attentive Variational Inference
Towards Empirical Sandwich Bounds on the Rate-Distortion Function
Clean Images are Hard to Reblur: Exploiting the Ill-Posed Inverse Task for Dynamic Scene Deblurring
Data Efficient Language-Supervised Zero-Shot Recognition with Optimal Transport Distillation
Unsupervised Discovery of Object Radiance Fields
A Class of Short-term Recurrence Anderson Mixing Methods and Their Applications
Efficient Self-supervised Vision Transformers for Representation Learning
On the Role of Neural Collapse in Transfer Learning
Optimization and Adaptive Generalization of Three layer Neural Networks
On the Importance of Firth Bias Reduction in Few-Shot Classification
Memorizing Transformers
On the role of population heterogeneity in emergent communication
Plant 'n' Seek: Can You Find the Winning Ticket?
Neural Stochastic Dual Dynamic Programming
Discrete Representations Strengthen Vision Transformer Robustness
You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks
Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization
GPT-Critic: Offline Reinforcement Learning for End-to-End Task-Oriented Dialogue Systems
Bootstrapped Meta-Learning
Pseudo-Labeled Auto-Curriculum Learning for Semi-Supervised Keypoint Localization
Efficient Sharpness-aware Minimization for Improved Training of Neural Networks
Bandit Learning with Joint Effect of Incentivized Sampling, Delayed Sampling Feedback, and Self-Reinforcing User Preferences
Efficient Neural Causal Discovery without Acyclicity Constraints
Path Auxiliary Proposal for MCMC in Discrete Space
PI3NN: Out-of-distribution-aware Prediction Intervals from Three Neural Networks
Approximation and Learning with Deep Convolutional Models: a Kernel Perspective
Fairness Guarantees under Demographic Shift
Do deep networks transfer invariances across classes?
Resolving Training Biases via Influence-based Data Relabeling
Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage
On feature learning in neural networks with global convergence guarantees
Case-based reasoning for better generalization in textual reinforcement learning
Assessing Generalization of SGD via Disagreement
Churn Reduction via Distillation
Frequency-aware SGD for Efficient Embedding Learning with Provable Benefits
Finding an Unsupervised Image Segmenter in each of your Deep Generative Models
PAC Prediction Sets Under Covariate Shift
FP-DETR: Detection Transformer Advanced by Fully Pre-training
Generalized Kernel Thinning
Graph Neural Network Guided Local Search for the Traveling Salesperson Problem
Toward Efficient Low-Precision Training: Data Format Optimization and Hysteresis Quantization
Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations
Amortized Implicit Differentiation for Stochastic Bilevel Optimization
Neural Models for Output-Space Invariance in Combinatorial Problems
Memory Augmented Optimizers for Deep Learning
Tracking the risk of a deployed model and detecting harmful distribution shifts
ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning
Learning-Augmented $k$-means Clustering
Latent Variable Sequential Set Transformers for Joint Multi-Agent Motion Prediction
It Takes Four to Tango: Multiagent Self Play for Automatic Curriculum Generation
Wish you were here: Hindsight Goal Selection for long-horizon dexterous manipulation
Spike-inspired rank coding for fast and accurate recurrent neural networks
Attention-based Interpretability with Concept Transformers
Information-theoretic Online Memory Selection for Continual Learning
On Improving Adversarial Transferability of Vision Transformers
Programmatic Reinforcement Learning without Oracles
CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability
Learning transferable motor skills with hierarchical latent mixture policies
Responsible Disclosure of Generative Models Using Scalable Fingerprinting
A Fine-Tuning Approach to Belief State Modeling
Decentralized Learning for Overparameterized Problems: A Multi-Agent Kernel Approximation Approach
Learning to Annotate Part Segmentation with Gradient Matching
Collapse by Conditioning: Training Class-conditional GANs with Limited Data
Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS Benchmarks
DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals
Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis
On the Certified Robustness for Ensemble Models and Beyond
Visual hyperacuity with moving sensor and recurrent neural computations
Implicit Bias of Projected Subgradient Method Gives Provable Robust Recovery of Subspaces of Unknown Codimension
CLEVA-Compass: A Continual Learning Evaluation Assessment Compass to Promote Research Transparency and Comparability
GRAND++: Graph Neural Diffusion with A Source Term
Can an Image Classifier Suffice For Action Recognition?
AS-MLP: An Axial Shifted MLP Architecture for Vision
Fairness in Representation for Multilingual NLP: Insights from Controlled Experiments on Conditional Language Modeling
MT3: Multi-Task Multitrack Music Transcription
Information Prioritization through Empowerment in Visual Model-based RL
VOS: Learning What You Don't Know by Virtual Outlier Synthesis
Reducing Excessive Margin to Achieve a Better Accuracy vs. Robustness Trade-off
Pre-training Molecular Graph Representation with 3D Geometry
A General Analysis of Example-Selection for Stochastic Gradient Descent
Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification
Objects in Semantic Topology
Neural Spectral Marked Point Processes
Demystifying Limited Adversarial Transferability in Automatic Speech Recognition Systems
Label-Efficient Semantic Segmentation with Diffusion Models
EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression
Improving Mutual Information Estimation with Annealed and Energy-Based Bounds
An Autoregressive Flow Model for 3D Molecular Geometry Generation from Scratch
Learning Curves for Gaussian Process Regression with Power-Law Priors and Targets
Differentiable Gradient Sampling for Learning Implicit 3D Scene Reconstructions from a Single Image
Tighter Sparse Approximation Bounds for ReLU Neural Networks
The Uncanny Similarity of Recurrence and Depth
A Deep Variational Approach to Clustering Survival Data
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?
A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning
Continuous-Time Meta-Learning with Forward Mode Differentiation
Geometric Transformers for Protein Interface Contact Prediction
Who Is Your Right Mixup Partner in Positive and Unlabeled Learning
Generative Modeling with Optimal Transport Maps
MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining
Maximizing Ensemble Diversity in Deep Reinforcement Learning
ADAVI: Automatic Dual Amortized Variational Inference Applied To Pyramidal Bayesian Models
Monotonic Differentiable Sorting Networks
Interpretable Unsupervised Diversity Denoising and Artefact Removal
FedPara: Low-rank Hadamard Product for Communication-Efficient Federated Learning
Trivial or Impossible --- dichotomous data difficulty masks model differences (on ImageNet and beyond)
Neural Contextual Bandits with Deep Representation and Shallow Exploration
Learning Neural Contextual Bandits through Perturbed Rewards
Multi-Task Processes
The Role of Pretrained Representations for the OOD Generalization of RL Agents
Differentially Private Fine-tuning of Language Models
Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy
QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization
Resonance in Weight Space: Covariate Shift Can Drive Divergence of SGD with Momentum
Stiffness-aware neural network for learning Hamiltonian systems
Topological Graph Neural Networks
CoordX: Accelerating Implicit Neural Representation with a Split MLP Architecture
Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality
Meta Learning Low Rank Covariance Factors for Energy Based Deterministic Uncertainty
Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations
Sample and Computation Redistribution for Efficient Face Detection
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective
Closed-form Sample Probing for Learning Generative Models in Zero-shot Learning
Dive Deeper Into Integral Pose Regression
MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer
On the Convergence of the Monte Carlo Exploring Starts Algorithm for Reinforcement Learning
SHINE: SHaring the INverse Estimate from the forward pass for bi-level optimization and implicit models
Leveraging Automated Unit Tests for Unsupervised Code Translation
Explainable GNN-Based Models over Knowledge Graphs
Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization
SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations
IntSGD: Adaptive Floatless Compression of Stochastic Gradients
Understanding the Role of Self Attention for Efficient Speech Recognition
PAC-Bayes Information Bottleneck
When should agents explore?
Understanding and Leveraging Overparameterization in Recursive Value Estimation
Neural Collapse Under MSE Loss: Proximity to and Dynamics on the Central Path
Hindsight: Posterior-guided training of retrievers for improved open-ended generation
Imbedding Deep Neural Networks
PF-GNN: Differentiable particle filtering based approximation of universal graph representations
Mirror Descent Policy Optimization
Unrolling PALM for Sparse Semi-Blind Source Separation
MoReL: Multi-omics Relational Learning
Sparse DETR: Efficient End-to-End Object Detection with Learnable Sparsity
Hybrid Local SGD for Federated Learning with Heterogeneous Communications
How to Train Your MAML to Excel in Few-Shot Classification
Learning more skills through optimistic exploration
Understanding and Preventing Capacity Loss in Reinforcement Learning
Dealing with Non-Stationarity in MARL via Trust-Region Decomposition
Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics
Cross-Domain Imitation Learning via Optimal Transport
Prototypical Contrastive Predictive Coding
LoRA: Low-Rank Adaptation of Large Language Models
Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks
Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation
Axiomatic Explanations for Visual Search, Retrieval, and Similarity Learning
Optimizing Neural Networks with Gradient Lexicase Selection
Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm
Value Gradient weighted Model-Based Reinforcement Learning
Learning Value Functions from Undirected State-only Experience
Vector-quantized Image Modeling with Improved VQGAN
Toward Faithful Case-based Reasoning through Learning Prototypes in a Nearest Neighbor-friendly Space.
Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism
Transformers Can Do Bayesian Inference
Filtered-CoPhy: Unsupervised Learning of Counterfactual Physics in Pixel Space
Network Augmentation for Tiny Deep Learning
Optimal Transport for Causal Discovery
GLASS: GNN with Labeling Tricks for Subgraph Representation Learning
New Insights on Reducing Abrupt Representation Change in Online Continual Learning
Decoupled Adaptation for Cross-Domain Object Detection
No Parameters Left Behind: Sensitivity Guided Adaptive Learning Rate for Training Large Transformer Models
Visual Correspondence Hallucination
Bayesian Framework for Gradient Leakage
Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization
Learning curves for continual learning in neural networks: Self-knowledge transfer and forgetting
How many degrees of freedom do we need to train deep networks: a loss landscape perspective
Effective Model Sparsification by Scheduled Grow-and-Prune Methods
Finite-Time Convergence and Sample Complexity of Multi-Agent Actor-Critic Reinforcement Learning with Average Reward
Energy-Inspired Molecular Conformation Optimization
An Explanation of In-context Learning as Implicit Bayesian Inference
The Close Relationship Between Contrastive Learning and Meta-Learning
Learning Transferable Reward for Query Object Localization with Policy Adaptation
Exposing the Implicit Energy Networks behind Masked Language Models via Metropolis--Hastings
You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction
Shuffle Private Stochastic Convex Optimization
Maximum Entropy RL (Provably) Solves Some Robust RL Problems
Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral Stream
Autonomous Learning of Object-Centric Abstractions for High-Level Planning
Fortuitous Forgetting in Connectionist Networks
A Generalized Weighted Optimization Method for Computational Learning and Inversion
Learning Synthetic Environments and Reward Networks for Reinforcement Learning
What Makes Better Augmentation Strategies? Augment Difficult but Not too Different
Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling
FILM: Following Instructions in Language with Modular Methods
T-WaveNet: A Tree-Structured Wavelet Neural Network for Time Series Signal Analysis
Top-label calibration and multiclass-to-binary reductions
PEARL: Data Synthesis via Private Embeddings and Adversarial Reconstruction Learning
Leveraging unlabeled data to predict out-of-distribution performance
On the Limitations of Multimodal VAEs
Maximum n-times Coverage for Vaccine Design
Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features
TRAIL: Near-Optimal Imitation Learning with Suboptimal Data
The Geometry of Memoryless Stochastic Policy Optimization in Infinite-Horizon POMDPs
A Theoretical Analysis on Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features
Recursive Disentanglement Network
Givens Coordinate Descent Methods for Rotation Matrix Learning in Trainable Embedding Indexes
Multi-objective Optimization by Learning Space Partition
Neural Structured Prediction for Inductive Node Classification
Knowledge Removal in Sampling-based Bayesian Inference
Einops: Clear and Reliable Tensor Manipulations with Einstein-like Notation
Language-biased image classification: evaluation based on semantic representations
What’s Wrong with Deep Learning in Tree Search for Combinatorial Optimization
Convergent and Efficient Deep Q Learning Algorithm
Constrained Policy Optimization via Bayesian World Models
Quadtree Attention for Vision Transformers
FastSHAP: Real-Time Shapley Value Estimation
Robust and Scalable SDE Learning: A Functional Perspective
CURVATURE-GUIDED DYNAMIC SCALE NETWORKS FOR MULTI-VIEW STEREO
StyleAlign: Analysis and Applications of Aligned StyleGAN Models
Autonomous Reinforcement Learning: Formalism and Benchmarking
Understanding over-squashing and bottlenecks on graphs via curvature
Exploring extreme parameter compression for pre-trained language models
Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks
Pix2seq: A Language Modeling Framework for Object Detection
AEVA: Black-box Backdoor Detection Using Adversarial Extreme Value Analysis
Scale Efficiently: Insights from Pretraining and Finetuning Transformers
Noisy Feature Mixup
Pretrained Language Model in Continual Learning: A Comparative Study
Fooling Explanations in Text Classifiers
Large Language Models Can Be Strong Differentially Private Learners
Data Poisoning Won’t Save You From Facial Recognition
Adaptive Wavelet Transformer Network for 3D Shape Representation Learning
Evidential Turing Processes
Invariant Causal Representation Learning for Out-of-Distribution Generalization
Enhancing Cross-lingual Transfer by Manifold Mixup
Why Propagate Alone? Parallel Use of Labels and Features on Graphs
Progressive Distillation for Fast Sampling of Diffusion Models
Iterated Reasoning with Mutual Information in Cooperative and Byzantine Decentralized Teaming
How Low Can We Go: Trading Memory for Error in Low-Precision Training
Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective
Active Hierarchical Exploration with Stable Subgoal Representation Learning
SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning
Permutation-Based SGD: Is Random Optimal?
Towards Continual Knowledge Learning of Language Models
GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation
Learning to Complete Code with Sketches
Dynamic Token Normalization improves Vision Transformers
Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions
Learning Discrete Structured Variational Auto-Encoder using Natural Evolution Strategies
Memory Replay with Data Compression for Continual Learning
VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objects
Goal-Directed Planning via Hindsight Experience Replay
NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphs
On the Convergence of mSGD and AdaGrad for Stochastic Optimization
Hybrid Random Features
Domino: Discovering Systematic Errors with Cross-Modal Embeddings
Learned Simulators for Turbulence
Scalable Sampling for Nonsymmetric Determinantal Point Processes
Deep AutoAugment
Towards Understanding the Robustness Against Evasion Attack on Categorical Data
Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies
Diffusion-Based Voice Conversion with Fast Maximum Likelihood Sampling Scheme
Auto-Transfer: Learning to Route Transferable Representations
Hierarchical Few-Shot Imitation with Skill Transition Models
How Attentive are Graph Attention Networks?
FairCal: Fairness Calibration for Face Verification
Provably convergent quasistatic dynamics for mean-field two-player zero-sum games
ProtoRes: Proto-Residual Network for Pose Authoring via Learned Inverse Kinematics
Sparse Attention with Learning to Hash
Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning
Optimizer Amalgamation
Entroformer: A Transformer-based Entropy Model for Learned Image Compression
Continual Learning with Recursive Gradient Optimization
HyAR: Addressing Discrete-Continuous Action Reinforcement Learning via Hybrid Action Representation
OntoProtein: Protein Pretraining With Gene Ontology Embedding
Permutation Compressors for Provably Faster Distributed Nonconvex Optimization
CrossBeam: Learning to Search in Bottom-Up Program Synthesis
RvS: What is Essential for Offline RL via Supervised Learning?
Learning Continuous Environment Fields via Implicit Functions
Fast AdvProp
Revisiting flow generative models for Out-of-distribution detection
DISCOVERING AND EXPLAINING THE REPRESENTATION BOTTLENECK OF DNNS
SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning
Parallel Training of GRU Networks with a Multi-Grid Solver for Long Sequences
Poisoning and Backdooring Contrastive Learning
iFlood: A Stable and Effective Regularizer
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction
Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients
When, Why, and Which Pretrained GANs Are Useful?
Near-Optimal Reward-Free Exploration for Linear Mixture MDPs with Plug-in Solver
RelaxLoss: Defending Membership Inference Attacks without Losing Utility
Compositional Attention: Disentangling Search and Retrieval
Anomaly Detection for Tabular Data with Internal Contrastive Learning
Variational Inference for Discriminative Learning with Generative Modeling of Feature Incompletion
Stability Regularization for Discrete Representation Learning
Fixed Neural Network Steganography: Train the images, not the network
Bregman Gradient Policy Optimization
X-model: Improving Data Efficiency in Deep Learning with A Minimax Model
Low-Budget Active Learning via Wasserstein Distance: An Integer Programming Approach
Target-Side Input Augmentation for Sequence to Sequence Generation
Graph Condensation for Graph Neural Networks
GreaseLM: Graph REASoning Enhanced Language Models
Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics
MonoDistill: Learning Spatial Features for Monocular 3D Object Detection
Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL
Language-driven Semantic Segmentation
Constructing Orthogonal Convolutions in an Explicit Manner
HTLM: Hyper-Text Pre-Training and Prompting of Language Models
Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting
BadPre: Task-agnostic Backdoor Attacks to Pre-trained NLP Foundation Models
Mind the Gap: Domain Gap Control for Single Shot Domain Adaptation for Generative Adversarial Networks
Neural Methods for Logical Reasoning over Knowledge Graphs
Data-Driven Offline Optimization for Architecting Hardware Accelerators
Towards Better Understanding and Better Generalization of Low-shot Classification in Histology Images with Contrastive Learning
Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios
Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios
Learning Versatile Neural Architectures by Propagating Network Codes
A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement Learning
Continuously Discovering Novel Strategies via Reward-Switching Policy Optimization
Dynamics-Aware Comparison of Learned Reward Functions
In a Nutshell, the Human Asked for This: Latent Goals for Following Temporal Specifications
Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?
Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future
Direct then Diffuse: Incremental Unsupervised Skill Discovery for State Covering and Goal Reaching
Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations
Sound and Complete Neural Network Repair with Minimality and Locality Guarantees
Discriminative Similarity for Data Clustering
Generalized Demographic Parity for Group Fairness
Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions
StyleNeRF: A Style-based 3D Aware Generator for High-resolution Image Synthesis
Distribution Compression in Near-Linear Time
On the Connection between Local Attention and Dynamic Depth-wise Convolution
Explanations of Black-Box Models based on Directional Feature Interactions
Language model compression with weighted low-rank factorization
Prototype memory and attention mechanisms for few shot image generation
Surrogate Gap Minimization Improves Sharpness-Aware Training
Optimal Representations for Covariate Shift
Bundle Networks: Fiber Bundles, Local Trivializations, and a Generative Approach to Exploring Many-to-one Maps
Anytime Dense Prediction with Confidence Adaptivity
Trigger Hunting with a Topological Prior for Trojan Detection
Graph-Guided Network for Irregularly Sampled Multivariate Time Series
SketchODE: Learning neural sketch representation in continuous time
Convergent Graph Solvers
MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling
From Intervention to Domain Transportation: A Novel Perspective to Optimize Recommendation
Concurrent Adversarial Learning for Large-Batch Training
CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale Attention
Properties from mechanisms: an equivariance perspective on identifiable representation learning
A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training
Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory
On the Pitfalls of Analyzing Individual Neurons in Language Models
Graph Neural Networks with Learnable Structural and Positional Representations
Step-unrolled Denoising Autoencoders for Text Generation
Sparse Communication via Mixed Distributions
Chemical-Reaction-Aware Molecule Representation Learning
CrowdPlay: Crowdsourcing Human Demonstrations for Offline Learning
Adversarial Retriever-Ranker for Dense Text Retrieval
Tuformer: Data-driven Design of Transformers for Improved Generalization or Efficiency
Handling Distribution Shifts on Graphs: An Invariance Perspective
Effect of scale on catastrophic forgetting in neural networks
Fast Differentiable Matrix Square Root
Topologically Regularized Data Embeddings
Neural Variational Dropout Processes
Learning Disentangled Representation by Exploiting Pretrained Generative Models: A Contrastive Learning View
Privacy Implications of Shuffling
Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design
Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information
Proof Artifact Co-Training for Theorem Proving with Language Models
Non-Parallel Text Style Transfer with Self-Parallel Supervision
A global convergence theory for deep ReLU implicit networks via over-parameterization
R5: Rule Discovery with Reinforced and Recurrent Relational Reasoning
Attacking deep networks with surrogate-based adversarial black-box methods is easy
Revisiting Over-smoothing in BERT from the Perspective of Graph
Neural Network Approximation based on Hausdorff distance of Tropical Zonotopes
Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain Analysis: From Theory to Practice
VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
On-Policy Model Errors in Reinforcement Learning
Signing the Supermask: Keep, Hide, Invert
Diverse Client Selection for Federated Learning via Submodular Maximization
Unifying Likelihood-free Inference with Black-box Optimization and Beyond
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation
Transformer Embeddings of Irregularly Spaced Events and Their Participants
ViDT: An Efficient and Effective Fully Transformer-based Object Detector
NETWORK INSENSITIVITY TO PARAMETER NOISE VIA PARAMETER ATTACK DURING TRAINING
Rethinking Adversarial Transferability from a Data Distribution Perspective
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks
Sequential Reptile: Inter-Task Gradient Alignment for Multilingual Learning
Neural graphical modelling in continuous-time: consistency guarantees and algorithms
Capturing Structural Locality in Non-parametric Language Models
Distilling GANs with Style-Mixed Triplets for X2I Translation with Limited Data
EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits
On the Existence of Universal Lottery Tickets
Sampling with Mirrored Stein Operators
A Statistical Framework for Efficient Out of Distribution Detection in Deep Neural Networks
Discovering Latent Concepts Learned in BERT
Provable Adaptation across Multiway Domains via Representation Learning
Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification
Group equivariant neural posterior estimation
Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration
Phase Collapse in Neural Networks
Federated Learning from Only Unlabeled Data with Class-conditional-sharing Clients
Message Passing Neural PDE Solvers
It Takes Two to Tango: Mixup for Deep Metric Learning
Communication-Efficient Actor-Critic Methods for Homogeneous Markov Games
Self-supervised Learning is More Robust to Dataset Imbalance
Contrastive Clustering to Mine Pseudo Parallel Data for Unsupervised Translation
A fast and accurate splitting method for optimal transport: analysis and implementation
Task-Induced Representation Learning
Triangle and Four Cycle Counting with Predictions in Graph Streams
ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods
How unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis
Expressiveness and Approximation Properties of Graph Neural Networks
How to deal with missing data in supervised deep learning?
Possibility Before Utility: Learning And Using Hierarchical Affordances
Autoregressive Diffusion Models
Expressivity of Emergent Languages is a Trade-off between Contextual Complexity and Unpredictability
Actor-Critic Policy Optimization in a Large-Scale Imperfect-Information Game
Fast Regression for Structured Inputs
Learning Efficient Online 3D Bin Packing on Packing Configuration Trees
Learning Long-Term Reward Redistribution via Randomized Return Decomposition
Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networks
Learning Strides in Convolutional Neural Networks
Data-Efficient Graph Grammar Learning for Molecular Generation
Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect
Certified Robustness for Deep Equilibrium Models via Interval Bound Propagation
On the Generalization of Models Trained with SGD: Information-Theoretic Bounds and Implications
ARTEMIS: Attention-based Retrieval with Text-Explicit Matching and Implicit Similarity
Learning Fast Samplers for Diffusion Models by Differentiating Through Sample Quality
Explaining Point Processes by Learning Interpretable Temporal Logic Rules
On Evaluation Metrics for Graph Generative Models
Probabilistic Implicit Scene Completion
Training Structured Neural Networks Through Manifold Identification and Variance Reduction
Natural Language Descriptions of Deep Features
Learning Pruning-Friendly Networks via Frank-Wolfe: One-Shot, Any-Sparsity, And No Retraining
Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain
miniF2F: a cross-system benchmark for formal Olympiad-level mathematics
Controlling Directions Orthogonal to a Classifier
Pareto Policy Pool for Model-based Offline Reinforcement Learning
Learning a subspace of policies for online adaptation in Reinforcement Learning
Neural Parameter Allocation Search
A Unified Wasserstein Distributional Robustness Framework for Adversarial Training
Fair Normalizing Flows
Self-Joint Supervised Learning
BiBERT: Accurate Fully Binarized BERT
LIGS: Learnable Intrinsic-Reward Generation Selection for Multi-Agent Learning
An Unconstrained Layer-Peeled Perspective on Neural Collapse
Hierarchical Variational Memory for Few-shot Learning Across Domains
Scarf: Self-Supervised Contrastive Learning using Random Feature Corruption
What Happens after SGD Reaches Zero Loss? --A Mathematical Framework
Symbolic Learning to Optimize: Towards Interpretability and Scalability
PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior
Revisit Kernel Pruning with Lottery Regulated Grouped Convolutions
Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting
Improving Non-Autoregressive Translation Models Without Distillation
Evaluating Disentanglement of Structured Representations
Sqrt(d) Dimension Dependence of Langevin Monte Carlo
Neural Solvers for Fast and Accurate Numerical Optimal Control
Comparing Distributions by Measuring Differences that Affect Decision Making
Graph-less Neural Networks: Teaching Old MLPs New Tricks Via Distillation
Score-Based Generative Modeling with Critically-Damped Langevin Diffusion
Learning by Directional Gradient Descent
Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery
An Information Fusion Approach to Learning with Instance-Dependent Label Noise
Joint Shapley values: a measure of joint feature importance
Weighted Training for Cross-Task Learning
Adversarial Unlearning of Backdoors via Implicit Hypergradient
Pareto Set Learning for Neural Multi-Objective Combinatorial Optimization
Ada-NETS: Face Clustering via Adaptive Neighbour Discovery in the Structure Space
Learning Representation from Neural Fisher Kernel with Low-rank Approximation
Actor-critic is implicitly biased towards high entropy optimal policies
Self-Supervised Inference in State-Space Models
Overcoming The Spectral Bias of Neural Value Approximation
Automatic Loss Function Search for Predict-Then-Optimize Problems with Strong Ranking Property
IFR-Explore: Learning Inter-object Functional Relationships in 3D Indoor Scenes
Learning to Extend Molecular Scaffolds with Structural Motifs
Lossless Compression with Probabilistic Circuits
SGD Can Converge to Local Maxima
Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddings
Meta-Imitation Learning by Watching Video Demonstrations
WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection
Differentiable DAG Sampling
Hyperparameter Tuning with Renyi Differential Privacy
Understanding approximate and unrolled dictionary learning for pattern recovery
Constraining Linear-chain CRFs to Regular Languages
Conditional Contrastive Learning with Kernel
Evading Adversarial Example Detection Defenses with Orthogonal Projected Gradient Descent
Imitation Learning by Reinforcement Learning
Multi-Agent MDP Homomorphic Networks
Spread Spurious Attribute: Improving Worst-group Accuracy with Spurious Attribute Estimation
Nonlinear ICA Using Volume-Preserving Transformations
Online Hyperparameter Meta-Learning with Hypergradient Distillation
Relating transformers to models and neural representations of the hippocampal formation
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions
Bayesian Neural Network Priors Revisited
AdaAug: Learning Class- and Instance-adaptive Data Augmentation Policies
Procedural generalization by planning with self-supervised world models
EigenGame Unloaded: When playing games is better than optimizing
End-to-End Learning of Probabilistic Hierarchies on Graphs
Asymmetry Learning for Counterfactually-invariant Classification in OOD Tasks
Dropout Q-Functions for Doubly Efficient Reinforcement Learning
Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection
DeSKO: Stability-Assured Robust Control with a Deep Stochastic Koopman Operator
Differentially Private Fractional Frequency Moments Estimation with Polylogarithmic Space
On the Optimal Memorization Power of ReLU Neural Networks
Model Agnostic Interpretability for Multiple Instance Learning
GNN-LM: Language Modeling based on Global Contexts via GNN
Fast Generic Interaction Detection for Model Interpretability and Compression
Towards Understanding the Data Dependency of Mixup-style Training
DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting
Safe Neurosymbolic Learning with Differentiable Symbolic Execution
Gradient Importance Learning for Incomplete Observations
A Biologically Interpretable Graph Convolutional Network to Link Genetic Risk Pathways and Imaging Phenotypes of Disease
Equivariant Subgraph Aggregation Networks
CoBERL: Contrastive BERT for Reinforcement Learning
Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs
Unsupervised Disentanglement with Tensor Product Representations on the Torus
Multi-Mode Deep Matrix and Tensor Factorization
Zero-Shot Self-Supervised Learning for MRI Reconstruction
NASPY: Automated Extraction of Automated Machine Learning Models
LEARNING GUARANTEES FOR GRAPH CONVOLUTIONAL NETWORKS ON THE STOCHASTIC BLOCK MODEL
Should I Run Offline Reinforcement Learning or Behavioral Cloning?
Mention Memory: incorporating textual knowledge into Transformers through entity mention attention
Learning Features with Parameter-Free Layers
Multi-Critic Actor Learning: Teaching RL Policies to Act with Style
GradMax: Growing Neural Networks using Gradient Information
Critical Points in Quantum Generative Models
ComPhy: Compositional Physical Reasoning of Objects and Events from Videos
Transferable Adversarial Attack based on Integrated Gradients
DKM: Differentiable k-Means Clustering Layer for Neural Network Compression
Scale Mixtures of Neural Network Gaussian Processes
Reward Uncertainty for Exploration in Preference-based Reinforcement Learning
Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains
Policy Smoothing for Provably Robust Reinforcement Learning
FlexConv: Continuous Kernel Convolutions With Differentiable Kernel Sizes
CKConv: Continuous Kernel Convolution For Sequential Data
RISP: Rendering-Invariant State Predictor with Differentiable Simulation and Rendering for Cross-Domain Parameter Estimation
On the Convergence of Certified Robust Training with Interval Bound Propagation
Rethinking Goal-Conditioned Supervised Learning and Its Connection to Offline RL
Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models
On Distributed Adaptive Optimization with Gradient Compression
Sequence Approximation using Feedforward Spiking Neural Network for Spatiotemporal Learning: Theory and Optimization Methods
Zero-CL: Instance and Feature decorrelation for negative-free symmetric contrastive learning
Salient ImageNet: How to discover spurious features in Deep Learning?
Differentiable Expectation-Maximization for Set Representation Learning
Offline Reinforcement Learning with Implicit Q-Learning
Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks
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