Research
 

NeurIPS

All Work

A Family of Robust Stochastic Operators for Reinforcement Learning
A Family of Robust Stochastic Operators for Reinforcement Learning
NeurIPS
 
How Transferable are Video Representations Based on Synthetic Data?
How Transferable are Video Representations Based on Synthetic Data?
NeurIPS
 
3D Concept Grounding on Neural Fields
3D Concept Grounding on Neural Fields
NeurIPS
 
Learning Neural Acoustic Fields
Learning Neural Acoustic Fields
NeurIPS
 
Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation
Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation
NeurIPS
 
Learning Active Camera for Multi-Object Navigation
Learning Active Camera for Multi-Object Navigation
NeurIPS
 
Losses Can Be Blessings: Routing Self-Supervised Speech Representations Towards Efficient Multilingual and Multitask Speech Processing
Losses Can Be Blessings: Routing Self-Supervised Speech Representations Towards Efficient Multilingual and Multitask Speech Processing
NeurIPS
 
Fairness Reprogramming
Fairness Reprogramming
NeurIPSAI Fairness
 
The Missing Invariance Principle found — the Reciprocal Twin of Invariant Risk Minimization
The Missing Invariance Principle found — the Reciprocal Twin of Invariant Risk Minimization
NeurIPS
 
S3-NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpoint
S3-NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpoint
NeurIPSComputer Vision
 
Advancing Model Pruning via Bi-level Optimization
Advancing Model Pruning via Bi-level Optimization
NeurIPSOptimization
 
Deep Differentiable Logic Gate Networks
Deep Differentiable Logic Gate Networks
NeurIPS
 
Factored Adaptation for Non-stationary Reinforcement Learning
Factored Adaptation for Non-stationary Reinforcement Learning
NeurIPSReinforcement Learning
 
DualCoOp: Fast Adaptation to Multi-Label Recognition with Limited Annotations
DualCoOp: Fast Adaptation to Multi-Label Recognition with Limited Annotations
NeurIPS
 
Finding Differences Between Transformers and ConvNets Using Counterfactual Simulation Testing
Finding Differences Between Transformers and ConvNets Using Counterfactual Simulation Testing
NeurIPS
 
Domain Adaptation meets Individual Fairness. And they get along
Domain Adaptation meets Individual Fairness. And they get along
NeurIPSAI Fairness
 
Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees
Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees
NeurIPS
 
Convergent representations of computer programs in human and artificial neural networks
Convergent representations of computer programs in human and artificial neural networks
NeurIPSNeuroscience
 
Learning Physical Dynamics with Subequivariant Graph Neural Networks
Learning Physical Dynamics with Subequivariant Graph Neural Networks
NeurIPS
 
Redeeming Intrinsic Rewards via Constrained Optimization
Redeeming Intrinsic Rewards via Constrained Optimization
NeurIPSOptimization
 
Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks
Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks
NeurIPS
 
Faster Linear Algebra for Distance Matrices
Faster Linear Algebra for Distance Matrices
NeurIPS
 
k-Sliced Mutual Information: A Quantitative Study of Scalability with Dimension
k-Sliced Mutual Information: A Quantitative Study of Scalability with Dimension
NeurIPS
 
Bringing Image Scene Structure to Video via Frame-Clip Consistency of Object Tokens
Bringing Image Scene Structure to Video via Frame-Clip Consistency of Object Tokens
Computer VisionNeurIPS
 
FETA: Towards Specializing Foundation Models for Expert Task Applications
FETA: Towards Specializing Foundation Models for Expert Task Applications
NeurIPS
 
Influencing Long-Term Behavior in Multiagent Reinforcement Learning
Influencing Long-Term Behavior in Multiagent Reinforcement Learning
NeurIPS
 
Procedural Image Programs for Representation Learning
Procedural Image Programs for Representation Learning
NeurIPS
 
Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting
Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting
NeurIPS
 
On-Device Training Under 256KB Memory
On-Device Training Under 256KB Memory
NeurIPS
 
How hard are computer vision datasets? Calibrating dataset difficulty to viewing time
How hard are computer vision datasets? Calibrating dataset difficulty to viewing time
Computer VisionNeurIPS
 
SNAKE: Shape-aware Neural 3D Keypoint Field
SNAKE: Shape-aware Neural 3D Keypoint Field
NeurIPS
 
Debugging foundation models for bias
Debugging foundation models for bias
IBM Research
↗
A simpler path to better computer vision
A simpler path to better computer vision
MIT News
↗
A far-sighted approach to machine learning
A far-sighted approach to machine learning
MIT News
↗
This AI can harness sound to reveal the structure of unseen spaces
This AI can harness sound to reveal the structure of unseen spaces
Popular Science
↗
Perceptron: AI that sees with sound, learns to walk and predicts seismic physics
Perceptron: AI that sees with sound, learns to walk and predicts seismic physics
TechCrunch
↗
In machine learning, synthetic data can offer real performance improvements
In machine learning, synthetic data can offer real performance improvements
MIT News
↗
Learning on the edge
Learning on the edge
MIT News
↗
TinyML is bringing neural networks to small microcontrollers
TinyML is bringing neural networks to small microcontrollers
TechTalks
↗
Clever Compression of Some Neural Nets Improves Performance
Clever Compression of Some Neural Nets Improves Performance
IEEE Spectrum
↗
AI Researchers Fight Noise by Turning to Biology
AI Researchers Fight Noise by Turning to Biology
Quanta Magazine
↗
A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics
A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics
NeurIPS
 
Object DGCNN: 3D Object Detection using Dynamic Graphs
Object DGCNN: 3D Object Detection using Dynamic Graphs
NeurIPS
 
Efficient Generalization with Distributionally Robust Learning
Efficient Generalization with Distributionally Robust Learning
NeurIPS
 
Learning to Delegate for Large-scale Vehicle Routing
Learning to Delegate for Large-scale Vehicle Routing
NeurIPS
 
Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark
Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark
NeurIPS
 
Grammar-Based Grounded Lexicon Learning
Grammar-Based Grounded Lexicon Learning
NeurIPS
 
An Exact Characterization of the Generalization Error for the Gibbs Algorithm
An Exact Characterization of the Generalization Error for the Gibbs Algorithm
NeurIPS
 
Change Point Detection via Multivariate Singular Spectrum Analysis
Change Point Detection via Multivariate Singular Spectrum Analysis
NeurIPS
 
PerSim: Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents via Personalized Simulators
PerSim: Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents via Personalized Simulators
NeurIPS
 
Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning
Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning
NeurIPS
 
Noether networks: meta-learning useful conserved quantities
Noether networks: meta-learning useful conserved quantities
NeurIPS
 
IA-RED^2 : Interpretability-Aware Redundancy Reduction for Vision Transformers
IA-RED^2 : Interpretability-Aware Redundancy Reduction for Vision Transformers
NeurIPS
 
Machine learning speeds up vehicle routing
Machine learning speeds up vehicle routing
MIT News
↗
Generating a realistic 3D world
Generating a realistic 3D world
MIT News
↗
Toward speech recognition for uncommon spoken languages
Toward speech recognition for uncommon spoken languages
MIT News
↗
AI Algorithms Are Slimming Down to Fit in Your Fridge
AI Algorithms Are Slimming Down to Fit in Your Fridge
WIRED
↗
Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations
Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations
NeurIPS
 
Black Loans Matter: Fighting Bias for AI Fairness in Lending
Black Loans Matter: Fighting Bias for AI Fairness in Lending
AI FairnessAI in Finance
 
Researchers Figured Out How to Fit More AI Than Ever onto Internet of Things Microchips
Researchers Figured Out How to Fit More AI Than Ever onto Internet of Things Microchips
Morning Brew
↗
Is neuroscience the key to protecting AI from adversarial attacks?
Is neuroscience the key to protecting AI from adversarial attacks?
TechTalks
↗
Learning Physical Graph Representations from Visual Scenes
Learning Physical Graph Representations from Visual Scenes
NeurIPS
 
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms
NeurIPS
 
Testing Determinantal Point Processes
Testing Determinantal Point Processes
NeurIPS
 
Fairness in Streaming Submodular Maximization: Algorithms and Hardness
Fairness in Streaming Submodular Maximization: Algorithms and Hardness
NeurIPS
 
CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models
CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models
NeurIPS
 
Active Structure Learning of Causal DAGs via Directed Clique Trees
Active Structure Learning of Causal DAGs via Directed Clique Trees
Causal Inference
 
Adversarially-learned Inference via an Ensemble of Discrete Undirected Graphical Models
Adversarially-learned Inference via an Ensemble of Discrete Undirected Graphical Models
NeurIPS
 
Online Bayesian Goal Inference for Boundedly-Rational Planning Agents
Online Bayesian Goal Inference for Boundedly-Rational Planning Agents
NeurIPS
 
Sharp Representation Theorems for ReLU Networks with Precise Dependence on Depth
Sharp Representation Theorems for ReLU Networks with Precise Dependence on Depth
NeurIPS
 
Robust Federated Learning: The Case of Affine Distribution Shifts
Robust Federated Learning: The Case of Affine Distribution Shifts
Federated Learning
 
Differentiable Augmentation for Data-Efficient GAN Training
Differentiable Augmentation for Data-Efficient GAN Training
Optimization
 
Learning Restricted Boltzmann Machines With Sparse Latent Variables
Learning Restricted Boltzmann Machines With Sparse Latent Variables
NeurIPS
 
Debiased Contrastive Learning
Debiased Contrastive Learning
NeurIPS
 
Neuroscientists find a way to make object-recognition models perform better
Neuroscientists find a way to make object-recognition models perform better
MIT News
↗
Higher-Order Certification For Randomized Smoothing
Higher-Order Certification For Randomized Smoothing
Optimization
 
Entropic Causal Inference: Identifiability and Finite Sample Results
Entropic Causal Inference: Identifiability and Finite Sample Results
Causal Inference
 
Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning
Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning
Causal Inference
 
Applications of Common Entropy in Causal Inference
Applications of Common Entropy in Causal Inference
Causal Inference
 
Training Stronger Baselines for Learning to Optimize
Training Stronger Baselines for Learning to Optimize
Optimization
 
Asymptotic Guarantees for Generative Modeling based on the Smooth Wasserstein Distance
Asymptotic Guarantees for Generative Modeling based on the Smooth Wasserstein Distance
Bayesian Modeling
 
Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations
Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations
NeurIPSNeuroscience
 
Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment
Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment
NeurIPS
 
Auxiliary Task Reweighting for Minimum-data Learning
Auxiliary Task Reweighting for Minimum-data Learning
NeurIPS
 
Tiny Transfer Learning: Towards Memory-Efficient On-Device Learning
Tiny Transfer Learning: Towards Memory-Efficient On-Device Learning
Efficient AI
 
Continuous Regularized Wasserstein Barycenters
Continuous Regularized Wasserstein Barycenters
Optimal Transport
 
The Lottery Ticket Hypothesis for the Pre-trained BERT Networks
The Lottery Ticket Hypothesis for the Pre-trained BERT Networks
Efficient AI
 
Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes
Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes
NeurIPS
 
Uncertainty-Aware Learning for Zero-Shot Semantic Segmentation
Uncertainty-Aware Learning for Zero-Shot Semantic Segmentation
NeurIPS
 
AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning
AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning
Computer VisionMultimodal Learning
 
Approximate Cross-Validation for Structured Models
Approximate Cross-Validation for Structured Models
NeurIPS
 
MCUNet: Tiny Deep Learning on IoT Devices
MCUNet: Tiny Deep Learning on IoT Devices
Deep LearningEfficient AI
 
Universal Domain Adaptation through Self Supervision
Universal Domain Adaptation through Self Supervision
Transfer LearningUnsupervised Learning
 
System brings deep learning to “internet of things” devices
System brings deep learning to “internet of things” devices
MIT News
↗
Does enforcing fairness mitigate biases caused by subpopulation shift?
Does enforcing fairness mitigate biases caused by subpopulation shift?
NeurIPS
 
ObjectNet: A bias-controlled dataset object recognition
ObjectNet: A bias-controlled dataset object recognition
Computer VisionNeurIPS
 
Point-Voxel CNN for Efficient 3D Deep Learning
Point-Voxel CNN for Efficient 3D Deep Learning
NeurIPSEfficient AI
 
ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization
ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization
NeurIPS
 
Sobolev Independence Criterion
Sobolev Independence Criterion
NeurIPS
 
Private Testing of Distributions via Sample Permutations
Private Testing of Distributions via Sample Permutations
NeurIPS
 
Image Synthesis with a Single (Robust) Classifier
Image Synthesis with a Single (Robust) Classifier
NeurIPS
 
Learning New Tricks From Old Dogs: Multi-Source Transfer Learning From Pre-Trained Networks
Learning New Tricks From Old Dogs: Multi-Source Transfer Learning From Pre-Trained Networks
NeurIPS
 
A Game Theoretic Approach to Class-wise Selective Rationalization
A Game Theoretic Approach to Class-wise Selective Rationalization
NeurIPS
 
Hierarchical Optimal Transport for Document Representation
Hierarchical Optimal Transport for Document Representation
NeurIPS
 
Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions
Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions
NeurIPS
 
Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers
Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers
NeurIPS
 
Statistical Model Aggregation via Parameter Matching
Statistical Model Aggregation via Parameter Matching
NeurIPS
 
Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement
Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement
NeurIPS
 
ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
NeurIPS
 
Adversarial Examples Are Not Bugs, They Are Features
Adversarial Examples Are Not Bugs, They Are Features
NeurIPS
 
Sample Efficient Active Learning of Causal Trees
Sample Efficient Active Learning of Causal Trees
NeurIPS
 
Scalable inference of topic evolution via models for latent geometric structures
Scalable inference of topic evolution via models for latent geometric structures
NeurIPS
 
ZO-AdaMM: Derivative-free optimization for black-box problems
ZO-AdaMM: Derivative-free optimization for black-box problems
OptimizationNeurIPS
 
Cross-channel Communication Networks
Cross-channel Communication Networks
NeurIPSDeep Learning
 
Tight Certificates of Adversarial Robustness
Tight Certificates of Adversarial Robustness
CybersecurityNeurIPS
 
Learning and Testing Causal Models with Interventions
Learning and Testing Causal Models with Interventions
NeurIPS
 
The Limit Points of (Optimistic) Gradient Descent in Min-Max Optimization
The Limit Points of (Optimistic) Gradient Descent in Min-Max Optimization
NeurIPS
 
Direct Estimation of Differences in Causal Graphs
Direct Estimation of Differences in Causal Graphs
NeurIPS
 
Learning Libraries of Subroutines for Neurally–Guided Bayesian Program Induction
Learning Libraries of Subroutines for Neurally–Guided Bayesian Program Induction
NeurIPS
 
How Does Batch Normalization Help Optimization?
How Does Batch Normalization Help Optimization?
NeurIPS
 
HOGWILD!-Gibbs Can Be PanAccurate
HOGWILD!-Gibbs Can Be PanAccurate
NeurIPS
 
Efficient Neural Network Robustness Certification with General Activation Functions
Efficient Neural Network Robustness Certification with General Activation Functions
NeurIPS
 
Dialog-based Interactive Image Retrieval
Dialog-based Interactive Image Retrieval
NeurIPSComputer Vision