NeurIPS

All Work

AI Algorithms Are Slimming Down to Fit in Your Fridge
AI Algorithms Are Slimming Down to Fit in Your Fridge
WIRED
Black Loans Matter: Fighting Bias for AI Fairness in Lending
Black Loans Matter: Fighting Bias for AI Fairness in Lending
 
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
 
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms
 
Testing Determinantal Point Processes
Testing Determinantal Point Processes
 
Fairness in Streaming Submodular Maximization: Algorithms and Hardness
Fairness in Streaming Submodular Maximization: Algorithms and Hardness
 
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
 
Active Structure Learning of Causal DAGs via Directed Clique Trees
Active Structure Learning of Causal DAGs via Directed Clique Trees
 
Adversarially-learned Inference via an Ensemble of Discrete Undirected Graphical Models
Adversarially-learned Inference via an Ensemble of Discrete Undirected Graphical Models
 
Online Bayesian Goal Inference for Boundedly-Rational Planning Agents
Online Bayesian Goal Inference for Boundedly-Rational Planning Agents
 
Sharp Representation Theorems for ReLU Networks with Precise Dependence on Depth
Sharp Representation Theorems for ReLU Networks with Precise Dependence on Depth
 
Robust Federated Learning: The Case of Affine Distribution Shifts
Robust Federated Learning: The Case of Affine Distribution Shifts
 
Differentiable Augmentation for Data-Efficient GAN Training
Differentiable Augmentation for Data-Efficient GAN Training
 
Learning Restricted Boltzmann Machines With Sparse Latent Variables
Learning Restricted Boltzmann Machines With Sparse Latent Variables
 
Debiased Contrastive Learning
Debiased Contrastive Learning
 
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
 
Entropic Causal Inference: Identifiability and Finite Sample Results
Entropic Causal Inference: Identifiability and Finite Sample Results
 
Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning
Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning
 
Applications of Common Entropy in Causal Inference
Applications of Common Entropy in Causal Inference
 
Training Stronger Baselines for Learning to Optimize
Training Stronger Baselines for Learning to Optimize
 
Asymptotic Guarantees for Generative Modeling based on the Smooth Wasserstein Distance
Asymptotic Guarantees for Generative Modeling based on the Smooth Wasserstein Distance
 
Simulating a Primate Visual Cortex at the Front of CNNs Improves Robustness to Adversarial Attacks and Image Corruptions
Simulating a Primate Visual Cortex at the Front of CNNs Improves Robustness to Adversarial Attacks and Image Corruptions
 
Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment
Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment
 
Auxiliary Task Reweighting for Minimum-data Learning
Auxiliary Task Reweighting for Minimum-data Learning
 
Tiny Transfer Learning: Towards Memory-Efficient On-Device Learning
Tiny Transfer Learning: Towards Memory-Efficient On-Device Learning
 
Continuous Regularized Wasserstein Barycenters
Continuous Regularized Wasserstein Barycenters
 
The Lottery Ticket Hypothesis for the Pre-trained BERT Networks
The Lottery Ticket Hypothesis for the Pre-trained BERT Networks
 
Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes
Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes
 
Uncertainty-Aware Learning for Zero-Shot Semantic Segmentation
Uncertainty-Aware Learning for Zero-Shot Semantic Segmentation
 
AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning
AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning
 
Approximate Cross-Validation for Structured Models
Approximate Cross-Validation for Structured Models
 
MCUNet: Tiny Deep Learning on IoT Devices
MCUNet: Tiny Deep Learning on IoT Devices
 
System brings deep learning to “internet of things” devices
System brings deep learning to “internet of things” devices
MIT News
ObjectNet: A bias-controlled dataset object recognition
ObjectNet: A bias-controlled dataset object recognition
 
ZO-AdaMM: Derivative-free optimization for black-box problems
ZO-AdaMM: Derivative-free optimization for black-box problems
 
Tight Certificates of Adversarial Robustness
Tight Certificates of Adversarial Robustness