Search Results for author: Peng Yan

Found 9 papers, 3 papers with code

A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions

no code implementations11 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.

Anomaly Detection energy management +3

Personalization Disentanglement for Federated Learning: An explainable perspective

no code implementations6 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.

Disentanglement Personalized Federated Learning

When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions

no code implementations22 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.

Attribute Federated Learning +1

Graph-guided Personalization for Federated Recommendation

no code implementations13 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.

Dual Personalization on Federated Recommendation

1 code implementation16 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.

Privacy Preserving Recommendation Systems

Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising

3 code implementations18 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.

Multi-Task Learning

Magnonic frequency comb through nonlinear magnon-skyrmion scattering

no code implementations4 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

Spin-wave focusing induced skyrmion generation

no code implementations17 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

Long short-term memory networks in memristor crossbars

1 code implementation30 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

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