Deep Frequency Principle Towards Understanding Why Deeper Learning Is Faster. - Zhiqin John Xu; Hanxu Zhou (AAAI, 2021)

Towards Understanding the Condensation of Two-layer Neural Networks at Initial Training. - Zhi-Qin John Xu; Hanxu Zhou; Tao Luo 012; Yaoyu Zhang (CoRR, 2021)

A variance principle explains why dropout finds flatter minima. - Zhongwang Zhang; Hanxu Zhou; Zhi-Qin John Xu (CoRR, 2021)

Empirical Phase Diagram for Three-layer Neural Networks with Infinite Width. - Hanxu Zhou; Qixuan Zhou; Zhenyuan Jin; Tao Luo 012; Yaoyu Zhang; Zhi-Qin John Xu (NeurIPS, 2022)

Towards Understanding the Condensation of Neural Networks at Initial Training. - Hanxu Zhou; Qixuan Zhou; Tao Luo 012; Yaoyu Zhang; Zhi-Qin John Xu (NeurIPS, 2022)

Understanding the Initial Condensation of Convolutional Neural Networks. - Zhangchen Zhou; Hanxu Zhou; Yuqing Li; Zhi-Qin John Xu (CoRR, 2023)

White-box Multimodal Jailbreaks Against Large Vision-Language Models. - Ruofan Wang; Xingjun Ma; Hanxu Zhou; Chuanjun Ji; Guangnan Ye; Yu-Gang Jiang 001 (ACM Multimedia, 2024)

Understanding Time Series Anomaly State Detection through One-Class Classification. - Hanxu Zhou; Yuan Zhang; Guangjie Leng; Ruofan Wang; Zhi-Qin John Xu (CoRR, 2024)