Kaixiong Zhou

papers

511

total citations

papers (14)

Towards Deeper Graph Neural Networks with Differentiable Group Normalization

NEURIPS 2020arXiv

224

citations

Dirichlet Energy Constrained Learning for Deep Graph Neural Networks

NEURIPS 2021arXiv

149

citations

A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking

NEURIPS 2022arXiv

citations

DivAug: Plug-In Automated Data Augmentation With Explicit Diversity Maximization

ICCV 2021arXiv

citations

Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model

NEURIPS 2023arXiv

citations

DAMO: Decoding by Accumulating Activations Momentum for Mitigating Hallucinations in Vision-Language Models

ICLR 2025

citations

Molecular Data Programming: Towards Molecule Pseudo-labeling with Systematic Weak Supervision

CVPR 2024

citations

Kaixiong Zhou

papers (14)

Towards Deeper Graph Neural Networks with Differentiable Group Normalization

Dirichlet Energy Constrained Learning for Deep Graph Neural Networks

A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking

DivAug: Plug-In Automated Data Augmentation With Explicit Diversity Maximization

Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model

DAMO: Decoding by Accumulating Activations Momentum for Mitigating Hallucinations in Vision-Language Models

Rethinking Independent Cross-Entropy Loss For Graph-Structured Data

Flexible Group Count Enables Hassle-Free Structured Pruning

TVE: Learning Meta-attribution for Transferable Vision Explainer

Detecting Interactions from Neural Networks via Topological Analysis

Knowledge Graphs Can be Learned with Just Intersection Features

GNNs Also Deserve Editing, and They Need It More Than Once

Soft Prompt Recovers Compressed LLMs, Transferably

Molecular Data Programming: Towards Molecule Pseudo-labeling with Systematic Weak Supervision

papers (14)

Towards Deeper Graph Neural Networks with Differentiable Group Normalization

Dirichlet Energy Constrained Learning for Deep Graph Neural Networks

A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking

DivAug: Plug-In Automated Data Augmentation With Explicit Diversity Maximization

Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model

DAMO: Decoding by Accumulating Activations Momentum for Mitigating Hallucinations in Vision-Language Models

Rethinking Independent Cross-Entropy Loss For Graph-Structured Data

Flexible Group Count Enables Hassle-Free Structured Pruning

TVE: Learning Meta-attribution for Transferable Vision Explainer

Detecting Interactions from Neural Networks via Topological Analysis

Knowledge Graphs Can be Learned with Just Intersection Features

GNNs Also Deserve Editing, and They Need It More Than Once

Soft Prompt Recovers Compressed LLMs, Transferably

Molecular Data Programming: Towards Molecule Pseudo-labeling with Systematic Weak Supervision