A Survey on Dropout Methods and Experimental Verification in Recommendation-Reference-Cited by-同舟云学术

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A Survey on Dropout Methods and Experimental Verification in Recommendation

Published:2022 Issue: Volume: Page:1-20
ISSN:1041-4347
Container-title:IEEE Transactions on Knowledge and Data Engineering
language:
Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

Li Yangkun¹^ORCID,Ma Weizhi²,Chen Chong¹^ORCID,Zhang Min¹,Liu Yiqun¹^ORCID,Ma Shaoping¹,Yang Yuekui³

Affiliation:

1. Department of Computer Science and Technology, Institute for Artificial Intelligence, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China

2. Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China

3. Tencent AI Platform Department, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/69/4358933/09810538.pdf?arnumber=9810538

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Fusion of standard and ordinal dropout techniques to regularise deep models;Information Fusion;2024-06

2. Informative (Dis)information: Exploring the Correlation Between Social Media Disinformation Campaigns and Real-World Criminal Activity;2024 5th International Conference in Electronic Engineering, Information Technology & Education (EEITE);2024-05-29

3. Anomaly Detection of Zero-Day Attacks Based on CNN and Regularization Techniques;Electronics;2023-01-23

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