Skip to yearly menu bar
Skip to main content
Main Navigation
ICLR
Help/FAQ
Contact ICLR
Downloads
ICLR Blog
Code of Conduct
Privacy Policy
Create Profile
Reset Password
Journal To Conference Track
Diversity & Inclusion
Proceedings at OpenReview
Future Meetings
Press
Exhibitor Information
ICLR Twitter
About ICLR
My Stuff
Login
Select Year: (2020)
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
Schedule
Workshops
Community
Socials
Town Hall
Mentorship
Sponsor Hall
Main Conference
Invited Talks
Papers
Awards
Town Hall
Organizers
Browse
mini
compact
topic
detail
Showing papers for
.
×
×
title
author
topic
session
shuffle
by
serendipity
bookmarked first
visited first
not visited first
bookmarked but not visited
Enable Javascript in your browser to see the papers page.
Computation Reallocation for Object Detection
At Stability's Edge: How to Adjust Hyperparameters to Preserve Minima Selection in Asynchronous Training of Neural Networks?
A Closer Look at the Optimization Landscapes of Generative Adversarial Networks
Hamiltonian Generative Networks
Stochastic Weight Averaging in Parallel: Large-Batch Training That Generalizes Well
Convergence of Gradient Methods on Bilinear Zero-Sum Games
Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies
Global Relational Models of Source Code
Continual learning with hypernetworks
Environmental drivers of systematicity and generalization in a situated agent
An Exponential Learning Rate Schedule for Deep Learning
Understanding the Limitations of Conditional Generative Models
ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning
Adversarial AutoAugment
Neural Machine Translation with Universal Visual Representation
Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?
Once for All: Train One Network and Specialize it for Efficient Deployment
Dynamical Distance Learning for Semi-Supervised and Unsupervised Skill Discovery
Differentiation of Blackbox Combinatorial Solvers
Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets
Dynamically Pruned Message Passing Networks for Large-scale Knowledge Graph Reasoning
Counterfactuals uncover the modular structure of deep generative models
Fast Neural Network Adaptation via Parameter Remapping and Architecture Search
Towards Stabilizing Batch Statistics in Backward Propagation of Batch Normalization
Learning to Group: A Bottom-Up Framework for 3D Part Discovery in Unseen Categories
Action Semantics Network: Considering the Effects of Actions in Multiagent Systems
Kernelized Wasserstein Natural Gradient
Learning from Explanations with Neural Execution Tree
Variance Reduction With Sparse Gradients
Batch-shaping for learning conditional channel gated networks
Self-Supervised Learning of Appliance Usage
CAQL: Continuous Action Q-Learning
Domain Adaptive Multibranch Networks
Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning
Mirror-Generative Neural Machine Translation
FSNet: Compression of Deep Convolutional Neural Networks by Filter Summary
Explain Your Move: Understanding Agent Actions Using Salient and Relevant Feature Attribution
Convolutional Conditional Neural Processes
Regularizing activations in neural networks via distribution matching with the Wasserstein metric
VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation
Deep Orientation Uncertainty Learning based on a Bingham Loss
Scale-Equivariant Steerable Networks
The intriguing role of module criticality in the generalization of deep networks
A Theoretical Analysis of the Number of Shots in Few-Shot Learning
Graph Neural Networks Exponentially Lose Expressive Power for Node Classification
Provable Filter Pruning for Efficient Neural Networks
Option Discovery using Deep Skill Chaining
Deep Symbolic Superoptimization Without Human Knowledge
State Alignment-based Imitation Learning
Mogrifier LSTM
Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control
Target-Embedding Autoencoders for Supervised Representation Learning
Fair Resource Allocation in Federated Learning
Causal Discovery with Reinforcement Learning
Geom-GCN: Geometric Graph Convolutional Networks
Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling
Sampling-Free Learning of Bayesian Quantized Neural Networks
On the Relationship between Self-Attention and Convolutional Layers
A Generalized Training Approach for Multiagent Learning
Simple and Effective Regularization Methods for Training on Noisily Labeled Data with Generalization Guarantee
Towards Verified Robustness under Text Deletion Interventions
Mixed Precision DNNs: All you need is a good parametrization
On Computation and Generalization of Generative Adversarial Imitation Learning
Demystifying Inter-Class Disentanglement
Progressive Learning and Disentanglement of Hierarchical Representations
Transferable Perturbations of Deep Feature Distributions
Hypermodels for Exploration
AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures
Semi-Supervised Generative Modeling for Controllable Speech Synthesis
You Only Train Once: Loss-Conditional Training of Deep Networks
Ranking Policy Gradient
Understanding and Robustifying Differentiable Architecture Search
On the interaction between supervision and self-play in emergent communication
Knowledge Consistency between Neural Networks and Beyond
Capsules with Inverted Dot-Product Attention Routing
Variational Autoencoders for Highly Multivariate Spatial Point Processes Intensities
Towards Fast Adaptation of Neural Architectures with Meta Learning
Stochastic Conditional Generative Networks with Basis Decomposition
Guiding Program Synthesis by Learning to Generate Examples
HiLLoC: lossless image compression with hierarchical latent variable models
Estimating counterfactual treatment outcomes over time through adversarially balanced representations
Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators
Identifying through Flows for Recovering Latent Representations
Learning Efficient Parameter Server Synchronization Policies for Distributed SGD
Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards
Strategies for Pre-training Graph Neural Networks
Decoupling Representation and Classifier for Long-Tailed Recognition
Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks
Accelerating SGD with momentum for over-parameterized learning
PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction
Inductive and Unsupervised Representation Learning on Graph Structured Objects
GraphSAINT: Graph Sampling Based Inductive Learning Method
Non-Autoregressive Dialog State Tracking
Disentangling neural mechanisms for perceptual grouping
A Probabilistic Formulation of Unsupervised Text Style Transfer
MEMO: A Deep Network for Flexible Combination of Episodic Memories
Neural Stored-program Memory
Asymptotics of Wide Networks from Feynman Diagrams
Optimistic Exploration even with a Pessimistic Initialisation
Gradient Descent Maximizes the Margin of Homogeneous Neural Networks
Duration-of-Stay Storage Assignment under Uncertainty
Continual Learning with Bayesian Neural Networks for Non-Stationary Data
Language GANs Falling Short
Neural Tangents: Fast and Easy Infinite Neural Networks in Python
Fooling Detection Alone is Not Enough: Adversarial Attack against Multiple Object Tracking
Gap-Aware Mitigation of Gradient Staleness
Finite Depth and Width Corrections to the Neural Tangent Kernel
Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space
Maximum Likelihood Constraint Inference for Inverse Reinforcement Learning
SCALOR: Generative World Models with Scalable Object Representations
ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring
Graph Constrained Reinforcement Learning for Natural Language Action Spaces
Learning Robust Representations via Multi-View Information Bottleneck
Dynamics-Aware Unsupervised Skill Discovery
Ridge Regression: Structure, Cross-Validation, and Sketching
Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks
Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction Detection
End to End Trainable Active Contours via Differentiable Rendering
Learning Disentangled Representations for CounterFactual Regression
Symplectic Recurrent Neural Networks
RNA Secondary Structure Prediction By Learning Unrolled Algorithms
BinaryDuo: Reducing Gradient Mismatch in Binary Activation Network by Coupling Binary Activations
Training individually fair ML models with sensitive subspace robustness
Mixed-curvature Variational Autoencoders
The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget
Meta-Learning with Warped Gradient Descent
Towards a Deep Network Architecture for Structured Smoothness
Making Efficient Use of Demonstrations to Solve Hard Exploration Problems
Locally Constant Networks
Phase Transitions for the Information Bottleneck in Representation Learning
Lookahead: A Far-sighted Alternative of Magnitude-based Pruning
Decentralized Deep Learning with Arbitrary Communication Compression
Fast is better than free: Revisiting adversarial training
Structured Object-Aware Physics Prediction for Video Modeling and Planning
Fast Task Inference with Variational Intrinsic Successor Features
Ae-Ot: A New Generative Model Based on Extended Semi-Discrete Optimal Transport
Generative Ratio Matching Networks
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
The Early Phase of Neural Network Training
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
Pay Attention to Features, Transfer Learn Faster CNNs
Few-shot Text Classification with Distributional Signatures
Memory-Based Graph Networks
On the Equivalence between Positional Node Embeddings and Structural Graph Representations
Sub-policy Adaptation for Hierarchical Reinforcement Learning
DBA: Distributed Backdoor Attacks against Federated Learning
Gradient-Based Neural DAG Learning
Scalable Neural Methods for Reasoning With a Symbolic Knowledge Base
A closer look at the approximation capabilities of neural networks
Black-Box Adversarial Attack with Transferable Model-based Embedding
Online and stochastic optimization beyond Lipschitz continuity: A Riemannian approach
Self-labelling via simultaneous clustering and representation learning
Classification-Based Anomaly Detection for General Data
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
RNNs Incrementally Evolving on an Equilibrium Manifold: A Panacea for Vanishing and Exploding Gradients?
Implementing Inductive bias for different navigation tasks through diverse RNN attrractors
Neural Text Generation With Unlikelihood Training
Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps
Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness
Spike-based causal inference for weight alignment
Poly-encoders: Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring
Empirical Studies on the Properties of Linear Regions in Deep Neural Networks
Expected Information Maximization: Using the I-Projection for Mixture Density Estimation
Meta-learning curiosity algorithms
Actor-Critic Provably Finds Nash Equilibria of Linear-Quadratic Mean-Field Games
Learning Execution Through Neural Code Fusion
Emergence of functional and structural properties of the head direction system by optimization of recurrent neural networks
Implicit Bias of Gradient Descent based Adversarial Training on Separable Data
Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering
Learning from Rules Generalizing Labeled Exemplars
Measuring Compositional Generalization: A Comprehensive Method on Realistic Data
SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference
Automated Relational Meta-learning
Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs
RaPP: Novelty Detection with Reconstruction along Projection Pathway
Neural Execution of Graph Algorithms
Compositional Language Continual Learning
Observational Overfitting in Reinforcement Learning
Understanding Knowledge Distillation in Non-autoregressive Machine Translation
Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees
TabFact: A Large-scale Dataset for Table-based Fact Verification
Neural Arithmetic Units
Reconstructing continuous distributions of 3D protein structure from cryo-EM images
Generalization of Two-layer Neural Networks: An Asymptotic Viewpoint
SELF: Learning to Filter Noisy Labels with Self-Ensembling
Robust Reinforcement Learning for Continuous Control with Model Misspecification
GenDICE: Generalized Offline Estimation of Stationary Values
Unsupervised Model Selection for Variational Disentangled Representation Learning
Robust training with ensemble consensus
Improved Sample Complexities for Deep Neural Networks and Robust Classification via an All-Layer Margin
Functional vs. parametric equivalence of ReLU networks
Robust Subspace Recovery Layer for Unsupervised Anomaly Detection
A Constructive Prediction of the Generalization Error Across Scales
Learning deep graph matching with channel-independent embedding and Hungarian attention
Learning To Explore Using Active Neural SLAM
BlockSwap: Fisher-guided Block Substitution for Network Compression on a Budget
Tranquil Clouds: Neural Networks for Learning Temporally Coherent Features in Point Clouds
Why Not to Use Zero Imputation? Correcting Sparsity Bias in Training Neural Networks
Rényi Fair Inference
SAdam: A Variant of Adam for Strongly Convex Functions
SPACE: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition
Hoppity: Learning Graph Transformations to Detect and Fix Bugs in Programs
Neural Symbolic Reader: Scalable Integration of Distributed and Symbolic Representations for Reading Comprehension
Adversarial Training and Provable Defenses: Bridging the Gap
Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation
Composing Task-Agnostic Policies with Deep Reinforcement Learning
NeurQuRI: Neural Question Requirement Inspector for Answerability Prediction in Machine Reading Comprehension
Lazy-CFR: fast and near-optimal regret minimization for extensive games with imperfect information
Semantically-Guided Representation Learning for Self-Supervised Monocular Depth
Vid2Game: Controllable Characters Extracted from Real-World Videos
Network Deconvolution
Pure and Spurious Critical Points: a Geometric Study of Linear Networks
Improving Generalization in Meta Reinforcement Learning using Learned Objectives
Meta Dropout: Learning to Perturb Latent Features for Generalization
A Theory of Usable Information under Computational Constraints
Gradientless Descent: High-Dimensional Zeroth-Order Optimization
Measuring and Improving the Use of Graph Information in Graph Neural Networks
Few-Shot Learning on Graphs via Super-Classes Based on Graph Spectral Measures
Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning
A Learning-based Iterative Method for Solving Vehicle Routing Problems
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness
Understanding the Limitations of Variational Mutual Information Estimators
Generalized Convolutional Forest Networks for Domain Generalization and Visual Recognition
Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning
Relational State-Space Model for Stochastic Multi-Object Systems
Deep Learning of Determinantal Point Processes via Proper Spectral Sub-gradient
Unrestricted Adversarial Examples via Semantic Manipulation
Data-Independent Neural Pruning via Coresets
Your classifier is secretly an energy based model and you should treat it like one
Generalization through Memorization: Nearest Neighbor Language Models
Piecewise linear activations substantially shape the loss surfaces of neural networks
Contrastive Learning of Structured World Models
On the Variance of the Adaptive Learning Rate and Beyond
Scalable Model Compression by Entropy Penalized Reparameterization
Ensemble Distribution Distillation
Low-Resource Knowledge-Grounded Dialogue Generation
Novelty Detection Via Blurring
A Signal Propagation Perspective for Pruning Neural Networks at Initialization
GLAD: Learning Sparse Graph Recovery
Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning
Rethinking the Hyperparameters for Fine-tuning
Dynamics-Aware Embeddings
Unbiased Contrastive Divergence Algorithm for Training Energy-Based Latent Variable Models
Robustness Verification for Transformers
Improved memory in recurrent neural networks with sequential non-normal dynamics
Locality and Compositionality in Zero-Shot Learning
Extreme Classification via Adversarial Softmax Approximation
Economy Statistical Recurrent Units For Inferring Nonlinear Granger Causality
The Gambler's Problem and Beyond
DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
Interpretable Complex-Valued Neural Networks for Privacy Protection
Geometric Insights into the Convergence of Nonlinear TD Learning
Learning Compositional Koopman Operators for Model-Based Control
On the Need for Topology-Aware Generative Models for Manifold-Based Defenses
Probabilistic Connection Importance Inference and Lossless Compression of Deep Neural Networks
Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision Activations
Robust anomaly detection and backdoor attack detection via differential privacy
Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents
Conditional Learning of Fair Representations
Infinite-Horizon Differentiable Model Predictive Control
Measuring the Reliability of Reinforcement Learning Algorithms
Disagreement-Regularized Imitation Learning
Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP
NAS evaluation is frustratingly hard
Curvature Graph Network
Compositional languages emerge in a neural iterated learning model
Depth-Adaptive Transformer
Plug and Play Language Models: A Simple Approach to Controlled Text Generation
NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search
Self-Adversarial Learning with Comparative Discrimination for Text Generation
Model Based Reinforcement Learning for Atari
Stochastic AUC Maximization with Deep Neural Networks
Compression based bound for non-compressed network: unified generalization error analysis of large compressible deep neural network
Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control
GAT: Generative Adversarial Training for Adversarial Example Detection and Classification
DeepSphere: a graph-based spherical CNN
Learning-Augmented Data Stream Algorithms
Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)
Drawing Early-Bird Tickets: Toward More Efficient Training of Deep Networks
The Shape of Data: Intrinsic Distance for Data Distributions
Implementation Matters in Deep RL: A Case Study on PPO and TRPO
Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets
Universal Approximation with Certified Networks
Deep Semi-Supervised Anomaly Detection
BayesOpt Adversarial Attack
Encoding word order in complex embeddings
N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks
To Relieve Your Headache of Training an MRF, Take AdVIL
Learning to Link
Federated Learning with Matched Averaging
BERTScore: Evaluating Text Generation with BERT
Dream to Control: Learning Behaviors by Latent Imagination
What graph neural networks cannot learn: depth vs width
Provable Benefit of Orthogonal Initialization in Optimizing Deep Linear Networks
Efficient Probabilistic Logic Reasoning with Graph Neural Networks
Breaking Certified Defenses: Semantic Adversarial Examples With Spoofed Robustness Certificates
Biologically inspired sleep algorithm for increased generalization and adversarial robustness in deep neural networks
Deep neuroethology of a virtual rodent
DeepHoyer: Learning Sparser Neural Network with Differentiable Scale-Invariant Sparsity Measures
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving
Dynamic Time Lag Regression: Predicting What & When
On Mutual Information Maximization for Representation Learning
Lite Transformer with Long-Short Range Attention
Adversarial Policies: Attacking Deep Reinforcement Learning
A critical analysis of self-supervision, or what we can learn from a single image
Discovering Motor Programs by Recomposing Demonstrations
PairNorm: Tackling Oversmoothing in GNNs
On the Global Convergence of Training Deep Linear ResNets
Defending Against Physically Realizable Attacks on Image Classification
Learning to Coordinate Manipulation Skills via Skill Behavior Diversification
Learning the Arrow of Time for Problems in Reinforcement Learning
Deep probabilistic subsampling for task-adaptive compressed sensing
Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks
Multiplicative Interactions and Where to Find Them
And the Bit Goes Down: Revisiting the Quantization of Neural Networks
Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning
Curriculum Loss: Robust Learning and Generalization against Label Corruption
PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search
On the Weaknesses of Reinforcement Learning for Neural Machine Translation
Contrastive Representation Distillation
Dynamic Model Pruning with Feedback
Sign Bits Are All You Need for Black-Box Attacks
Building Deep Equivariant Capsule Networks
U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation
B-Spline CNNs on Lie groups
Span Recovery for Deep Neural Networks with Applications to Input Obfuscation
Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language Model
Sliced Cramer Synaptic Consolidation for Preserving Deeply Learned Representations
Critical initialisation in continuous approximations of binary neural networks
Learn to Explain Efficiently via Neural Logic Inductive Learning
Transformer-XH: Multi-Evidence Reasoning with eXtra Hop Attention
SpikeGrad: An ANN-equivalent Computation Model for Implementing Backpropagation with Spikes
Massively Multilingual Sparse Word Representations
Deep Audio Priors Emerge From Harmonic Convolutional Networks
The Local Elasticity of Neural Networks
RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments
Uncertainty-guided Continual Learning with Bayesian Neural Networks
Differentiable Reasoning over a Virtual Knowledge Base
Understanding and Improving Information Transfer in Multi-Task Learning
StructPool: Structured Graph Pooling via Conditional Random Fields
Generative Models for Effective ML on Private, Decentralized Datasets
Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML
Energy-based models for atomic-resolution protein conformations
Abductive Commonsense Reasoning
Training binary neural networks with real-to-binary convolutions
Maxmin Q-learning: Controlling the Estimation Bias of Q-learning
Discriminative Particle Filter Reinforcement Learning for Complex Partial observations
Thieves on Sesame Street! Model Extraction of BERT-based APIs
High Fidelity Speech Synthesis with Adversarial Networks
Program Guided Agent
Sharing Knowledge in Multi-Task Deep Reinforcement Learning
Controlling generative models with continuous factors of variations
Federated Adversarial Domain Adaptation
Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control
AdvectiveNet: An Eulerian-Lagrangian Fluidic Reservoir for Point Cloud Processing
State-only Imitation with Transition Dynamics Mismatch
Lipschitz constant estimation of Neural Networks via sparse polynomial optimization
GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
A Fair Comparison of Graph Neural Networks for Graph Classification
Deep Imitative Models for Flexible Inference, Planning, and Control
Augmenting Non-Collaborative Dialog Systems with Explicit Semantic and Strategic Dialog History
Distance-Based Learning from Errors for Confidence Calibration
Training Recurrent Neural Networks Online by Learning Explicit State Variables
Query2box: Reasoning over Knowledge Graphs in Vector Space Using Box Embeddings
CoPhy: Counterfactual Learning of Physical Dynamics
ES-MAML: Simple Hessian-Free Meta Learning
Combining Q-Learning and Search with Amortized Value Estimates
Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints
Are Pre-trained Language Models Aware of Phrases? Simple but Strong Baselines for Grammar Induction
Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks
Towards Stable and Efficient Training of Verifiably Robust Neural Networks
Neural Policy Gradient Methods: Global Optimality and Rates of Convergence
The Break-Even Point on Optimization Trajectories of Deep Neural Networks
Adjustable Real-time Style Transfer
vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations
Learned Step Size Quantization
Low-dimensional statistical manifold embedding of directed graphs
Query-efficient Meta Attack to Deep Neural Networks
Efficient Riemannian Optimization on the Stiefel Manifold via the Cayley Transform
Learning Expensive Coordination: An Event-Based Deep RL Approach
Adversarial Lipschitz Regularization
Provable robustness against all adversarial $l_p$-perturbations for $p\geq 1$
Effect of Activation Functions on the Training of Overparametrized Neural Nets
Transferring Optimality Across Data Distributions via Homotopy Methods
Population-Guided Parallel Policy Search for Reinforcement Learning
Explanation by Progressive Exaggeration
Enhancing Adversarial Defense by k-Winners-Take-All
Enhancing Transformation-Based Defenses Against Adversarial Attacks with a Distribution Classifier
Making Sense of Reinforcement Learning and Probabilistic Inference
Tree-Structured Attention with Hierarchical Accumulation
Optimal Strategies Against Generative Attacks
Deep Network Classification by Scattering and Homotopy Dictionary Learning
Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics
Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models
On Universal Equivariant Set Networks
Neural tangent kernels, transportation mappings, and universal approximation
CLN2INV: Learning Loop Invariants with Continuous Logic Networks
Neural Epitome Search for Architecture-Agnostic Network Compression
Episodic Reinforcement Learning with Associative Memory
Improving Neural Language Generation with Spectrum Control
In Search for a SAT-friendly Binarized Neural Network Architecture
CLEVRER: Collision Events for Video Representation and Reasoning
Understanding Generalization in Recurrent Neural Networks
Harnessing Structures for Value-Based Planning and Reinforcement Learning
Multilingual Alignment of Contextual Word Representations
Mathematical Reasoning in Latent Space
Smooth markets: A basic mechanism for organizing gradient-based learners
Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework
Learning to solve the credit assignment problem
Permutation Equivariant Models for Compositional Generalization in Language
The Logical Expressiveness of Graph Neural Networks
Reducing Transformer Depth on Demand with Structured Dropout
On Identifiability in Transformers
Overlearning Reveals Sensitive Attributes
MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius
Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization
Sample Efficient Policy Gradient Methods with Recursive Variance Reduction
Mutual Information Gradient Estimation for Representation Learning
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning
Lagrangian Fluid Simulation with Continuous Convolutions
Pruned Graph Scattering Transforms
Influence-Based Multi-Agent Exploration
Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames
Four Things Everyone Should Know to Improve Batch Normalization
On Bonus Based Exploration Methods In The Arcade Learning Environment
Weakly Supervised Clustering by Exploiting Unique Class Count
Theory and Evaluation Metrics for Learning Disentangled Representations
A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer Learning
Rotation-invariant clustering of neuronal responses in primary visual cortex
Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generation
EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness Against Adversarial Attacks
Intrinsic Motivation for Encouraging Synergistic Behavior
An Inductive Bias for Distances: Neural Nets that Respect the Triangle Inequality
Towards Hierarchical Importance Attribution: Explaining Compositional Semantics for Neural Sequence Models
Data-dependent Gaussian Prior Objective for Language Generation
Intensity-Free Learning of Temporal Point Processes
GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding
Masked Based Unsupervised Content Transfer
Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation
Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives
A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms
Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks
Truth or backpropaganda? An empirical investigation of deep learning theory
Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks
Exploring Model-based Planning with Policy Networks
Deformable Kernels: Adapting Effective Receptive Fields for Object Deformation
Improving Adversarial Robustness Requires Revisiting Misclassified Examples
Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning
Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning
Coherent Gradients: An Approach to Understanding Generalization in Gradient Descent-based Optimization
FasterSeg: Searching for Faster Real-time Semantic Segmentation
Jacobian Adversarially Regularized Networks for Robustness
Distributed Bandit Learning: Near-Optimal Regret with Efficient Communication
Cross-Lingual Ability of Multilingual BERT: An Empirical Study
DivideMix: Learning with Noisy Labels as Semi-supervised Learning
Quantifying the Cost of Reliable Photo Authentication via High-Performance Learned Lossy Representations
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning
Monotonic Multihead Attention
Residual Energy-Based Models for Text Generation
Fantastic Generalization Measures and Where to Find Them
Adversarially robust transfer learning
Adversarially Robust Representations with Smooth Encoders
On Generalization Error Bounds of Noisy Gradient Methods for Non-Convex Learning
Graph inference learning for semi-supervised classification
Discrepancy Ratio: Evaluating Model Performance When Even Experts Disagree on the Truth
Revisiting Self-Training for Neural Sequence Generation
Imitation Learning via Off-Policy Distribution Matching
Iterative energy-based projection on a normal data manifold for anomaly localization
A Closer Look at Deep Policy Gradients
Tensor Decompositions for Temporal Knowledge Base Completion
Progressive Memory Banks for Incremental Domain Adaptation
IMPACT: Importance Weighted Asynchronous Architectures with Clipped Target Networks
AtomNAS: Fine-Grained End-to-End Neural Architecture Search
Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning
The Ingredients of Real World Robotic Reinforcement Learning
Frequency-based Search-control in Dyna
Exploration in Reinforcement Learning with Deep Covering Options
Projection-Based Constrained Policy Optimization
On Robustness of Neural Ordinary Differential Equations
Generalization bounds for deep convolutional neural networks
Learning to Control PDEs with Differentiable Physics
Real or Not Real, that is the Question
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
MMA Training: Direct Input Space Margin Maximization through Adversarial Training
Editable Neural Networks
Learning to Move with Affordance Maps
Model-Augmented Actor-Critic: Backpropagating through Paths
Multi-Agent Interactions Modeling with Correlated Policies
Model-based reinforcement learning for biological sequence design
Intriguing Properties of Adversarial Training at Scale
Behaviour Suite for Reinforcement Learning
Meta-Q-Learning
Deep Double Descent: Where Bigger Models and More Data Hurt
Understanding Architectures Learnt by Cell-based Neural Architecture Search
Extreme Tensoring for Low-Memory Preconditioning
Differentiable learning of numerical rules in knowledge graphs
Neural Network Branching for Neural Network Verification
Learning representations for binary-classification without backpropagation
NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search
Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity
Robust Local Features for Improving the Generalization of Adversarial Training
Shifted and Squeezed 8-bit Floating Point format for Low-Precision Training of Deep Neural Networks
A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning
Learning Space Partitions for Nearest Neighbor Search
Geometric Analysis of Nonconvex Optimization Landscapes for Overcomplete Learning
Principled Weight Initialization for Hypernetworks
Order Learning and Its Application to Age Estimation
Recurrent neural circuits for contour detection
Hyper-SAGNN: a self-attention based graph neural network for hypergraphs
Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions
DiffTaichi: Differentiable Programming for Physical Simulation
Efficient and Information-Preserving Future Frame Prediction and Beyond
Meta-Learning Deep Energy-Based Memory Models
Bayesian Meta Sampling for Fast Uncertainty Adaptation
Restricting the Flow: Information Bottlenecks for Attribution
Multi-agent Reinforcement Learning for Networked System Control
Composition-based Multi-Relational Graph Convolutional Networks
Gradient $\ell_1$ Regularization for Quantization Robustness
Towards neural networks that provably know when they don't know
Reanalysis of Variance Reduced Temporal Difference Learning
Quantum Algorithms for Deep Convolutional Neural Networks
Inductive representation learning on temporal graphs
Bounds on Over-Parameterization for Guaranteed Existence of Descent Paths in Shallow ReLU Networks
Consistency Regularization for Generative Adversarial Networks
Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation
Learning to Guide Random Search
Emergent Tool Use From Multi-Agent Autocurricula
Can gradient clipping mitigate label noise?
From Inference to Generation: End-to-end Fully Self-supervised Generation of Human Face from Speech
LAMOL: LAnguage MOdeling for Lifelong Language Learning
Sparse Coding with Gated Learned ISTA
FSPool: Learning Set Representations with Featurewise Sort Pooling
Pre-training Tasks for Embedding-based Large-scale Retrieval
Robust And Interpretable Blind Image Denoising Via Bias-Free Convolutional Neural Networks
DeepV2D: Video to Depth with Differentiable Structure from Motion
AMRL: Aggregated Memory For Reinforcement Learning
Learning to Represent Programs with Property Signatures
V4D: 4D Convolutional Neural Networks for Video-level Representation Learning
Selection via Proxy: Efficient Data Selection for Deep Learning
PCMC-Net: Feature-based Pairwise Choice Markov Chains
BackPACK: Packing more into Backprop
Kernel of CycleGAN as a principal homogeneous space
Higher-Order Function Networks for Learning Composable 3D Object Representations
Decoding As Dynamic Programming For Recurrent Autoregressive Models
Variational Recurrent Models for Solving Partially Observable Control Tasks
Learning Heuristics for Quantified Boolean Formulas through Reinforcement Learning
VL-BERT: Pre-training of Generic Visual-Linguistic Representations
On the Convergence of FedAvg on Non-IID Data
Never Give Up: Learning Directed Exploration Strategies
Depth-Width Trade-offs for ReLU Networks via Sharkovsky's Theorem
Deep Learning For Symbolic Mathematics
Incorporating BERT into Neural Machine Translation
Escaping Saddle Points Faster with Stochastic Momentum
Learning from Unlabelled Videos Using Contrastive Predictive Neural 3D Mapping
Unpaired Point Cloud Completion on Real Scans using Adversarial Training
Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers
ProxSGD: Training Structured Neural Networks under Regularization and Constraints
Single Episode Policy Transfer in Reinforcement Learning
Don't Use Large Mini-batches, Use Local SGD
Disentangling Factors of Variations Using Few Labels
A Latent Morphology Model for Open-Vocabulary Neural Machine Translation
Stable Rank Normalization for Improved Generalization in Neural Networks and GANs
A Framework for Robustness Certification of Smoothed Classifiers Using F-Divergences
Co-Attentive Equivariant Neural Networks: Focusing Equivariance On Transformations Co-Occurring in Data
Gradients as Features for Deep Representation Learning
Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
The asymptotic spectrum of the Hessian of DNN throughout training
Detecting Extrapolation with Local Ensembles
Spectral Embedding of Regularized Block Models
Empirical Bayes Transductive Meta-Learning with Synthetic Gradients
How much Position Information Do Convolutional Neural Networks Encode?
RGBD-GAN: Unsupervised 3D Representation Learning From Natural Image Datasets via RGBD Image Synthesis
Weakly Supervised Disentanglement with Guarantees
Directional Message Passing for Molecular Graphs
Information Geometry of Orthogonal Initializations and Training
BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning
Minimizing FLOPs to Learn Efficient Sparse Representations
Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel
A Mutual Information Maximization Perspective of Language Representation Learning
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
Diverse Trajectory Forecasting with Determinantal Point Processes
Graph Convolutional Reinforcement Learning
Picking Winning Tickets Before Training by Preserving Gradient Flow
Unsupervised Clustering using Pseudo-semi-supervised Learning
Learning transport cost from subset correspondence
Scalable and Order-robust Continual Learning with Additive Parameter Decomposition
Scaling Autoregressive Video Models
GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations
Understanding Why Neural Networks Generalize Well Through GSNR of Parameters
Linear Symmetric Quantization of Neural Networks for Low-precision Integer Hardware
Reformer: The Efficient Transformer
Automatically Discovering and Learning New Visual Categories with Ranking Statistics
Difference-Seeking Generative Adversarial Network--Unseen Sample Generation
Comparing Rewinding and Fine-tuning in Neural Network Pruning
I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively
Are Transformers universal approximators of sequence-to-sequence functions?
StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding
Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation
RTFM: Generalising to New Environment Dynamics via Reading
Certified Robustness for Top-k Predictions against Adversarial Perturbations via Randomized Smoothing
Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference
Analysis of Video Feature Learning in Two-Stream CNNs on the Example of Zebrafish Swim Bout Classification
You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings
SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models
Understanding l4-based Dictionary Learning: Interpretation, Stability, and Robustness
Inductive Matrix Completion Based on Graph Neural Networks
LambdaNet: Probabilistic Type Inference using Graph Neural Networks
Latent Normalizing Flows for Many-to-Many Cross-Domain Mappings
Conservative Uncertainty Estimation By Fitting Prior Networks
Compressive Transformers for Long-Range Sequence Modelling
word2ket: Space-efficient Word Embeddings inspired by Quantum Entanglement
Differentially Private Meta-Learning
Adaptive Structural Fingerprints for Graph Attention Networks
Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization
Infinite-horizon Off-Policy Policy Evaluation with Multiple Behavior Policies
Learning to Learn by Zeroth-Order Oracle
RaCT: Toward Amortized Ranking-Critical Training For Collaborative Filtering
Training Generative Adversarial Networks from Incomplete Observations using Factorised Discriminators
Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation
Learning The Difference That Makes A Difference With Counterfactually-Augmented Data
White Noise Analysis of Neural Networks
Intrinsically Motivated Discovery of Diverse Patterns in Self-Organizing Systems
Neural Outlier Rejection for Self-Supervised Keypoint Learning
Estimating Gradients for Discrete Random Variables by Sampling without Replacement
Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation
Reinforced active learning for image segmentation
On Solving Minimax Optimization Locally: A Follow-the-Ridge Approach
A Stochastic Derivative Free Optimization Method with Momentum
Input Complexity and Out-of-distribution Detection with Likelihood-based Generative Models
Learning Hierarchical Discrete Linguistic Units from Visually-Grounded Speech
Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue
Watch the Unobserved: A Simple Approach to Parallelizing Monte Carlo Tree Search
Physics-as-Inverse-Graphics: Unsupervised Physical Parameter Estimation from Video
Jelly Bean World: A Testbed for Never-Ending Learning
Posterior sampling for multi-agent reinforcement learning: solving extensive games with imperfect information
FreeLB: Enhanced Adversarial Training for Natural Language Understanding
Neural Module Networks for Reasoning over Text
SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum
AutoQ: Automated Kernel-Wise Neural Network Quantization
V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control
Meta-Learning without Memorization
DDSP: Differentiable Digital Signal Processing
A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case
What Can Neural Networks Reason About?
MetaPix: Few-Shot Video Retargeting
Functional Regularisation for Continual Learning with Gaussian Processes
Identity Crisis: Memorization and Generalization Under Extreme Overparameterization
Probability Calibration for Knowledge Graph Embedding Models
Thinking While Moving: Deep Reinforcement Learning with Concurrent Control
Smoothness and Stability in GANs
From Variational to Deterministic Autoencoders
SNOW: Subscribing to Knowledge via Channel Pooling for Transfer & Lifelong Learning of Convolutional Neural Networks
Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification
CATER: A diagnostic dataset for Compositional Actions & TEmporal Reasoning
Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks
How to 0wn the NAS in Your Spare Time
Variational Template Machine for Data-to-Text Generation
Deep Graph Matching Consensus
Certified Defenses for Adversarial Patches
The Curious Case of Neural Text Degeneration
Learning Nearly Decomposable Value Functions Via Communication Minimization
Short and Sparse Deconvolution --- A Geometric Approach
Deep 3D Pan via Local adaptive "t-shaped" convolutions with global and local adaptive dilations
Distributionally Robust Neural Networks
DeFINE: Deep Factorized Input Token Embeddings for Neural Sequence Modeling
Sign-OPT: A Query-Efficient Hard-label Adversarial Attack
Evaluating The Search Phase of Neural Architecture Search
A Baseline for Few-Shot Image Classification
Abstract Diagrammatic Reasoning with Multiplex Graph Networks
SNODE: Spectral Discretization of Neural ODEs for System Identification
On the "steerability" of generative adversarial networks
SVQN: Sequential Variational Soft Q-Learning Networks
Automated curriculum generation through setter-solver interactions
One-Shot Pruning of Recurrent Neural Networks by Jacobian Spectrum Evaluation
Synthesizing Programmatic Policies that Inductively Generalize
The Implicit Bias of Depth: How Incremental Learning Drives Generalization
Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks
Double Neural Counterfactual Regret Minimization
Continual Learning with Adaptive Weights (CLAW)
SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards
Logic and the 2-Simplicial Transformer
Image-guided Neural Object Rendering
We use cookies to store which papers have been visited.
I agree
Successful Page Load
ICLR uses cookies to remember that you are logged in. By using our websites, you agree to the placement of cookies.
Our Privacy Policy »
Accept Cookies
We use cookies to store which papers have been visited.
I agree