no code implementations • 15 Sep 2024 • Ming Li, Pengcheng Xu, Junjie Hu, Zeyu Tang, Guang Yang
Federated learning holds great potential for enabling large-scale healthcare research and collaboration across multiple centres while ensuring data privacy and security are not compromised.
no code implementations • 27 Mar 2024 • Ruoyu Zhao, Qingnan Fan, Fei Kou, Shuai Qin, Hong Gu, Wei Wu, Pengcheng Xu, Mingrui Zhu, Nannan Wang, Xinbo Gao
Two key techniques are introduced into InstructBrush, Attention-based Instruction Optimization and Transformation-oriented Instruction Initialization, to address the limitations of the previous method in terms of inversion effects and instruction generalization.
no code implementations • 9 Oct 2023 • Pengcheng Xu, Tao Feng, Tianfan Fu, Siddhartha Laghuvarapu, Jimeng Sun
In contrast to the traditional RNN-based models, our proposed method exhibits superior performance in generating compounds predicted to be active against various biological targets, capturing long-term dependencies in the molecular structure sequence.
no code implementations • 24 Jun 2023 • Pengcheng Xu, Jinpu Cai, Yulin Gao, Ziqi Rong
DNA methylation is a crucial regulator of gene transcription and has been linked to various diseases, including autoimmune diseases and cancers.
1 code implementation • 3 Feb 2023 • Pengcheng Xu, Boyu Wang, Charles Ling
We demonstrate that domain labels are not directly necessary for BTDA if categorical distributions of various domains are sufficiently aligned even facing the imbalance of domains and the label distribution shift of classes.
Ranked #1 on Multi-target Domain Adaptation on Office-Home
Blended-target Domain Adaptation Label shift of blended-target domain adaptation +1
no code implementations • 31 Jan 2023 • Li Yi, Gezheng Xu, Pengcheng Xu, Jiaqi Li, Ruizhi Pu, Charles Ling, A. Ian McLeod, Boyu Wang
We also prove that such a difference makes existing LLN methods that rely on their distribution assumptions unable to address the label noise in SFDA.
no code implementations • 8 Jul 2022 • Pengcheng Xu, Yunfeng Lu
Efficient and accurate remaining useful life prediction is a key factor for reliable and safe usage of lithium-ion batteries.
1 code implementation • 4 May 2021 • Qingcheng Xiao, Size Zheng, Bingzhe Wu, Pengcheng Xu, Xuehai Qian, Yun Liang
Second, the overall design space composed of HW/SW partitioning, hardware optimization, and software optimization is huge.
no code implementations • 27 Feb 2021 • Lei Wang, Pengcheng Xu, Zhaoyang Qu, Xiaoyong Bo, Yunchang Dong, Zhenming Zhang, Yang Li
Existing coordinated cyber-attack detection methods have low detection accuracy and efficiency and poor generalization ability due to difficulties dealing with unbalanced attack data samples, high data dimensionality, and noisy data sets.
no code implementations • 18 Sep 2019 • Pengcheng Xu, Prudhvi Gurram, Gene Whipps, Rama Chellappa
Prior approaches utilize adversarial training based on cross entropy between the source and target domain distributions to learn a shared feature mapping that minimizes the domain gap.
no code implementations • 12 Jun 2018 • Pinjia He, Jieming Zhu, Pengcheng Xu, Zibin Zheng, Michael R. Lyu
A typical log-based system reliability management procedure is to first parse log messages because of their unstructured format; and apply data mining techniques on the parsed logs to obtain critical system behavior information.
Software Engineering