8 citations · 16 across the 10 of their papers we have counts for
10 papers
Principal-Part Decomposition for Neural Operator Learning of Dirichlet-to-Neumann Maps
Shuo Ling, Wenjun Ying, Han Zhou
Dirichlet-to-Neumann (DtN) maps send boundary values of a partial differential equation (PDE) solution to its normal derivative on the boundary. Learning such maps across varying d…
DiffoR: A Unified Continuous Generative Framework for Universal Ordinal Regression
Hongxu Ma, Lin Wang, Chenghou Jin +6
Ordinal Regression (OR) aims to predict target values with inherent order, underpinning critical applications across diverse domains, from recommender systems to computer vision. T…
White-box Multimodal Jailbreaks Against Large Vision-Language Models
Ruofan Wang, Xingjun Ma, Hanxu Zhou +3
Recent advancements in Large Vision-Language Models (VLMs) have underscored their superiority in various multimodal tasks. However, the adversarial robustness of VLMs has not been…
Understanding Time Series Anomaly State Detection through One-Class Classification
Hanxu Zhou, Yuan Zhang, Guangjie Leng +2
For a long time, research on time series anomaly detection has mainly focused on finding outliers within a given time series. Admittedly, this is consistent with some practical pro…
Understanding the Initial Condensation of Convolutional Neural Networks
Zhangchen Zhou, Hanxu Zhou, Yuqing Li +1
Previous research has shown that fully-connected networks with small initialization and gradient-based training methods exhibit a phenomenon known as condensation during training.…
Combinatorial optimization for low bit-width neural networks
Han Zhou, Aida Ashrafi, Matthew B. Blaschko
Low-bit width neural networks have been extensively explored for deployment on edge devices to reduce computational resources. Existing approaches have focused on gradient-based op…
Empirical Phase Diagram for Three-layer Neural Networks with Infinite Width
Hanxu Zhou, Qixuan Zhou, Zhenyuan Jin +3
Substantial work indicates that the dynamics of neural networks (NNs) is closely related to their initialization of parameters. Inspired by the phase diagram for two-layer ReLU NNs…
Dropout in Training Neural Networks: Flatness of Solution and Noise Structure
Zhongwang Zhang, Hanxu Zhou, Zhi-Qin John Xu
It is important to understand how the popular regularization method dropout helps the neural network training find a good generalization solution. In this work, we show that the tr…
A Unified Pre-training Framework for Conversational AI
Siqi Bao, Bingjin Chen, Huang He +7
In this work, we explore the application of PLATO-2 on various dialogue systems, including open-domain conversation, knowledge grounded dialogue, and task-oriented conversation. PL…
Towards Understanding the Condensation of Neural Networks at Initial Training
Hanxu Zhou, Qixuan Zhou, Tao Luo +2
Empirical works show that for ReLU neural networks (NNs) with small initialization, input weights of hidden neurons (the input weight of a hidden neuron consists of the weight from…