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pro vyhledávání: '"Ha, Heonseok"'
Personalized federated learning is aimed at allowing numerous clients to train personalized models while participating in collaborative training in a communication-efficient manner without exchanging private data. However, many personalized federated
Externí odkaz:
http://arxiv.org/abs/2210.14226
Autor:
Jeong, Yonghyun, Choi, Jooyoung, Kim, Sungwon, Ro, Youngmin, Oh, Tae-Hyun, Kim, Doyeon, Ha, Heonseok, Yoon, Sungroh
In this work, we present Facial Identity Controllable GAN (FICGAN) for not only generating high-quality de-identified face images with ensured privacy protection, but also detailed controllability on attribute preservation for enhanced data utility.
Externí odkaz:
http://arxiv.org/abs/2110.00740
To promote secure and private artificial intelligence (SPAI), we review studies on the model security and data privacy of DNNs. Model security allows system to behave as intended without being affected by malicious external influences that can compro
Externí odkaz:
http://arxiv.org/abs/1807.11655
Knowledge tracing (KT), a key component of an intelligent tutoring system, is a machine learning technique that estimates the mastery level of a student based on his/her past performance. The objective of KT is to predict a student's response to the
Externí odkaz:
http://arxiv.org/abs/1805.10768
A recommender system aims to recommend items that a user is interested in among many items. The need for the recommender system has been expanded by the information explosion. Various approaches have been suggested for providing meaningful recommenda
Externí odkaz:
http://arxiv.org/abs/1801.05532
Autor:
Yoo, Jaeyoon, Ha, Heonseok, Yi, Jihun, Ryu, Jongha, Kim, Chanju, Ha, Jung-Woo, Kim, Young-Han, Yoon, Sungroh
Recommender systems aim to find an accurate and efficient mapping from historic data of user-preferred items to a new item that is to be liked by a user. Towards this goal, energy-based sequence generative adversarial nets (EB-SeqGANs) are adopted fo
Externí odkaz:
http://arxiv.org/abs/1706.09200
Publikováno v:
2016 IEEE 32nd International Conference on Data Engineering (ICDE); 2016, p743-754, 12p
Akademický článek
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