Search Results for author: Shufei Zhang

Found 15 papers, 6 papers with code

Dataset Distillers Are Good Label Denoisers In the Wild

1 code implementation18 Nov 2024 Lechao Cheng, KaiFeng Chen, Jiyang Li, Shengeng Tang, Shufei Zhang, Meng Wang

Learning from noisy data has become essential for adapting deep learning models to real-world applications.

Dataset Distillation Denoising

SiMilarity-Enhanced Homophily for Multi-View Heterophilous Graph Clustering

no code implementations4 Oct 2024 Jianpeng Chen, Yawen Ling, Yazhou Ren, Zichen Wen, Tianyi Wu, Shufei Zhang, Lifang He

With the increasing prevalence of graph-structured data, multi-view graph clustering has been widely used in various downstream applications.

Clustering Graph Clustering +1

LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning

1 code implementation3 Oct 2024 Di Zhang, Jianbo Wu, Jingdi Lei, Tong Che, Jiatong Li, Tong Xie, Xiaoshui Huang, Shufei Zhang, Marco Pavone, Yuqiang Li, Wanli Ouyang, Dongzhan Zhou

This paper presents an advanced mathematical problem-solving framework, LLaMA-Berry, for enhancing the mathematical reasoning ability of Large Language Models (LLMs).

Efficient Exploration Mathematical Problem-Solving +1

Perturbation Diversity Certificates Robust Generalisation

no code implementations29 Sep 2021 Zhuang Qian, Shufei Zhang, Kaizhu Huang, Qiufeng Wang, Bin Gu, Huan Xiong, Xinping Yi

It is possibly due to the fact that the conventional adversarial training methods generate adversarial perturbations usually in a supervised way, so that the adversarial samples are highly biased towards the decision boundary, resulting in an inhomogeneous data distribution.

Diversity

Improving Model Robustness with Latent Distribution Locally and Globally

1 code implementation8 Jul 2021 Zhuang Qian, Shufei Zhang, Kaizhu Huang, Qiufeng Wang, Rui Zhang, Xinping Yi

The proposed adversarial training with latent distribution (ATLD) method defends against adversarial attacks by crafting LMAEs with the latent manifold in an unsupervised manner.

Adversarial Robustness

Gradient Distribution Alignment Certificates Better Adversarial Domain Adaptation

1 code implementation ICCV 2021 Zhiqiang Gao, Shufei Zhang, Kaizhu Huang, Qiufeng Wang, Chaoliang Zhong

In particular, we show that the distribution discrepancy can be reduced by constraining feature gradients of two domains to have similar distributions.

Unsupervised Domain Adaptation

Robust Generative Adversarial Network

no code implementations ICLR 2020 Shufei Zhang, Zhuang Qian, Kai-Zhu Huang, Jimin Xiao, Yuan He

Generative adversarial networks (GANs) are powerful generative models, but usually suffer from instability and generalization problem which may lead to poor generations.

Generative Adversarial Network

On Model Robustness Against Adversarial Examples

no code implementations15 Nov 2019 Shufei Zhang, Kai-Zhu Huang, Zenglin Xu

We propose to exploit an energy function to describe the stability and prove that reducing such energy guarantees the robustness against adversarial examples.

model

LEARNING ADVERSARIAL EXAMPLES WITH RIEMANNIAN GEOMETRY

no code implementations ICLR 2019 Shufei Zhang, Kai-Zhu Huang, Rui Zhang, Amir Hussain

In this paper, we propose a generalized framework that addresses the learning problem of adversarial examples with Riemannian geometry.

Manifold Adversarial Learning

no code implementations16 Jul 2018 Shufei Zhang, Kai-Zhu Huang, Jianke Zhu, Yang Liu

All the existing adversarial training methods consider only how the worst perturbed examples (i. e., adversarial examples) could affect the model output.

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