no code implementations • 15 Nov 2023 • Zachary F. Fisher, Younghoon Kim, Vladas Pipiras, Christopher Crawford, Daniel J. Petrie, Michael D. Hunter, Charles F. Geier
How best to model structurally heterogeneous processes is a foundational question in the social, health and behavioral sciences.
no code implementations • 26 Sep 2023 • Junggu Choi, Kion Kim, Soohyun Park, Juyoen Hur, Hyunjung Yang, Younghoon Kim, Hakbae Lee, Sanghoon Han
Based on the experimental results, we confirm that QA-based algorithms have comparable capabilities in factor analysis research to the MLR models that have been widely used in previous studies.
no code implementations • 6 Mar 2023 • Kang Choi, Donghyun Son, Younghoon Kim, Jiwon Seo
To understand and debug convolutional neural networks (CNNs) we propose techniques for testing the channels of CNNs.
no code implementations • 9 Aug 2022 • Younghoon Kim, Tao Wang, Danyi Xiong, Xinlei Wang, Seongoh Park
Among different types of data used to answer this biological question, studies based on T cell receptors (TCRs) are under recent spotlight due to the growing appreciation of the roles of the host immunity system in tumor biology.
no code implementations • 24 May 2021 • Daniel Wontae Nam, Younghoon Kim, Chan Y. Park
In this paper, we devise a distributional framework on actor-critic as a solution to distributional instability, action type restriction, and conflation between samples and statistics.
1 code implementation • 7 May 2021 • Dongmyung Shin, Younghoon Kim, Chungseok Oh, Hongjun An, Juhyung Park, Jiye Kim, Jongho Lee
This work may lay the foundation for an emerging field of AI-driven RF waveform design.
no code implementations • 1 Jan 2021 • Daniel Wontae Nam, Younghoon Kim, Chan Youn Park
Recent distributional reinforcement learning methods, despite their successes, still contain fundamental problems that can lead to inaccurate representations of value distributions, such as distributional instability, action type restriction, and biased approximation.
1 code implementation • NeurIPS 2020 • Ildoo Kim, Younghoon Kim, Sungwoong Kim
Data augmentation has been actively studied for robust neural networks.
no code implementations • 9 Jul 2020 • Zachary F. Fisher, Younghoon Kim, Barbara Fredrickson, Vladas Pipiras
Despite these new opportunities psychological researchers have not taken full advantage of promising opportunities inherent to this data, the potential to forecast psychological processes at the individual level.
no code implementations • ICLR 2019 • Jisung Hwang, Younghoon Kim, Sanghyuk Chun, Jaejun Yoo, Ji-Hoon Kim, Dongyoon Han, Jung-Woo Ha
The checkerboard phenomenon is one of the well-known visual artifacts in the computer vision field.