Zobrazeno 1 - 10
of 58
pro vyhledávání: '"Heo, Geon"'
Generative models must ensure both privacy and fairness for Trustworthy AI. While these goals have been pursued separately, recent studies propose to combine existing privacy and fairness techniques to achieve both goals. However, naively combining t
Externí odkaz:
http://arxiv.org/abs/2410.02246
Personalized privacy becomes critical in deep learning for Trustworthy AI. While Differentially Private Stochastic Gradient Descent (DP-SGD) is widely used in deep learning methods supporting privacy, it provides the same level of privacy to all indi
Externí odkaz:
http://arxiv.org/abs/2305.15165
Autor:
Heo, Geon, Whang, Steven Euijong
Information leakage is becoming a critical problem as various information becomes publicly available by mistake, and machine learning models train on that data to provide services. As a result, one's private information could easily be memorized by s
Externí odkaz:
http://arxiv.org/abs/2202.02902
Autor:
Heo, Geon1 (AUTHOR), Kim, Seulgi2 (AUTHOR), Cho, Sung-il2,3 (AUTHOR), Yoo, Seunghyun2,3 (AUTHOR), Hwang, Jieun1 (AUTHOR) hwang0310@dankook.ac.kr
Publikováno v:
Tobacco Induced Diseases. Jul2024, Vol. 22, p1-12. 12p.
Autor:
Lim, Hyung-Jun, Kim, Gye Wan, Heo, Geon Hyeock, Jeong, Uidon, Kim, Min Jeong, Jeong, Dokyung, Hyun, Yoonsuk, Kim, Doory
Publikováno v:
In Biosensors and Bioelectronics 1 November 2024 263
Responsible AI is becoming critical as AI is widely used in our everyday lives. Many companies that deploy AI publicly state that when training a model, we not only need to improve its accuracy, but also need to guarantee that the model does not disc
Externí odkaz:
http://arxiv.org/abs/2101.05967
Publikováno v:
Proceedings of the VLDB Endowment, Volume 14, Issue 1, September 2020
As machine learning for images becomes democratized in the Software 2.0 era, one of the serious bottlenecks is securing enough labeled data for training. This problem is especially critical in a manufacturing setting where smart factories rely on mac
Externí odkaz:
http://arxiv.org/abs/2004.03264
Data collection is a major bottleneck in machine learning and an active research topic in multiple communities. There are largely two reasons data collection has recently become a critical issue. First, as machine learning is becoming more widely-use
Externí odkaz:
http://arxiv.org/abs/1811.03402
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