Chong You

Affiliations

Google DeepMind

papers

880

total citations

papers (12)

A Geometric Analysis of Neural Collapse with Unconstrained Features

NEURIPS 2021arXiv

236

citations

Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction

NEURIPS 2020arXiv

234

citations

Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-parameterization

NEURIPS 2020arXiv

citations

Incremental Learning via Rate Reduction

CVPR 2021arXiv

citations

Generalized Neural Collapse for a Large Number of Classes

ICML 2024arXiv

citations

Revisiting Sparse Convolutional Model for Visual Recognition

NEURIPS 2022arXiv

citations

Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training

NEURIPS 2021arXiv

citations

Scalable In-context Ranking with Generative Models

NEURIPS 2025arXiv

citations

Spark Transformer: Reactivating Sparsity in Transformer FFN and Attention

NEURIPS 2025

citations

Chong You

Affiliations

papers (12)

A Geometric Analysis of Neural Collapse with Unconstrained Features

Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction

Stochastic Sparse Subspace Clustering

Are All Losses Created Equal: A Neural Collapse Perspective

Learning a Self-Expressive Network for Subspace Clustering

Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-parameterization

Incremental Learning via Rate Reduction

Generalized Neural Collapse for a Large Number of Classes

Revisiting Sparse Convolutional Model for Visual Recognition

Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training

Scalable In-context Ranking with Generative Models

Spark Transformer: Reactivating Sparsity in Transformer FFN and Attention

papers (12)

A Geometric Analysis of Neural Collapse with Unconstrained Features

Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction

Stochastic Sparse Subspace Clustering

Are All Losses Created Equal: A Neural Collapse Perspective

Learning a Self-Expressive Network for Subspace Clustering

Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-parameterization

Incremental Learning via Rate Reduction

Generalized Neural Collapse for a Large Number of Classes

Revisiting Sparse Convolutional Model for Visual Recognition

Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training

Scalable In-context Ranking with Generative Models

Spark Transformer: Reactivating Sparsity in Transformer FFN and Attention