2 code implementations • 12 Apr 2024 • Dongbo Xi, Zhen Chen, Yuexian Wang, He Cui, Chong Peng, Fuzhen Zhuang, Peng Yan
Besides, by personalized integration of domain features from other domains for each user and the innovation in the training mode, the DFEI framework can yield more accurate conversion identification.
no code implementations • 3 Apr 2024 • Mozhi Zhang, Mianqiu Huang, Rundong Shi, Linsen Guo, Chong Peng, Peng Yan, Yaqian Zhou, Xipeng Qiu
Large language models optimized with techniques like RLHF have achieved good alignment in being helpful and harmless.
no code implementations • 28 Mar 2024 • Peng Yan, Guodong Long
Personalized Federated Learning (PerFL) is a new machine learning paradigm that delivers personalized models for diverse clients under federated learning settings.
no code implementations • 11 Jul 2023 • Peng Yan, Ahmed Abdulkadir, Paul-Philipp Luley, Matthias Rosenthal, Gerrit A. Schatte, Benjamin F. Grewe, Thilo Stadelmann
However, due to the dynamic nature of the industrial processes and environment, it is impractical to acquire large-scale labeled data for standard deep learning training for every slightly different case anew.
no code implementations • 6 Jun 2023 • Peng Yan, Guodong Long
Personalized federated learning (PFL) jointly trains a variety of local models through balancing between knowledge sharing across clients and model personalization per client.
no code implementations • 22 May 2023 • Chunxu Zhang, Guodong Long, Tianyi Zhou, Zijian Zhang, Peng Yan, Bo Yang
However, this separation of the recommendation model and users' private data poses a challenge in providing quality service, particularly when it comes to new items, namely cold-start recommendations in federated settings.
no code implementations • 13 May 2023 • Chunxu Zhang, Guodong Long, Tianyi Zhou, Peng Yan, Zijjian Zhang, Bo Yang
Federated Recommendation is a new service architecture providing recommendations without sharing user data with the server.
1 code implementation • 16 Jan 2023 • Chunxu Zhang, Guodong Long, Tianyi Zhou, Peng Yan, Zijian Zhang, Chengqi Zhang, Bo Yang
Moreover, we provide visualizations and in-depth analysis of the personalization techniques in item embedding, which shed novel insights on the design of recommender systems in federated settings.
3 code implementations • 18 May 2021 • Dongbo Xi, Zhen Chen, Peng Yan, Yinger Zhang, Yongchun Zhu, Fuzhen Zhuang, Yu Chen
While considerable multi-task efforts have been made in this direction, a long-standing challenge is how to explicitly model the long-path sequential dependence among audience multi-step conversions for improving the end-to-end conversion.
no code implementations • 4 Feb 2021 • Zhenyu Wang, H. Y. Yuan, Yunshan Cao, Z. -X. Li, Rembert A. Duine, Peng Yan
An optical frequency comb consists of a set of discrete and equally spaced frequencies and has found wide applications in the synthesis over broad spectral frequencies of electromagnetic wave and precise optical frequency metrology.
Mesoscale and Nanoscale Physics Optics
no code implementations • 17 Sep 2020 • Zhenyu Wang, Z. -X. Li, Ruifang Wang, Bo Liu, Hao Meng, Yunshan Cao, Peng Yan
We propose a new method to generate magnetic skyrmions through spin-wave focusing in chiral ferromagnets. A lens is constructed to focus spin waves by a curved interface between two ferromagnetic thin films with different perpendicular magnetic anisotropies.
Mesoscale and Nanoscale Physics
1 code implementation • 30 May 2018 • Can Li, Zhongrui Wang, Mingyi Rao, Daniel Belkin, Wenhao Song, Hao Jiang, Peng Yan, Yunning Li, Peng Lin, Miao Hu, Ning Ge, John Paul Strachan, Mark Barnell, Qing Wu, R. Stanley Williams, J. Joshua Yang, Qiangfei Xia
Recent breakthroughs in recurrent deep neural networks with long short-term memory (LSTM) units has led to major advances in artificial intelligence.
Emerging Technologies Applied Physics