Author
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Prof. Choi received a prestigious POSCO Open and Collaboration (O&C) Award at the 33rd POSCO Annual Technical Conference for his contribution of explainable AI research in the POSCO smart factory.
KAIST XAI center, led by Prof. Jaesik Choi, made an MOU with IBK Bank for the research and education of explainable AI.
A new book, An Introduction to Lifted Probabilistic Inference edited by Guy Van den Broeck, Kristian Kersting and Sriraam Natarajan, on probabilistic inference is available at MIT press. Prof. Jaesik Choi contributed to write two book chapters “6 Lifted Aggregation… Continue Reading →
Our joint work with Prof. Yuseong Jung’s lab at KAIST is accepted at Chemical Science [IF 9.825] Juhwan Kim, Geun Ho Gu, Juhwan Noh, Seongun Kim, Suji Gim, Jaesik Choi* and Yousung Jung*, Predicting Potentially Hazardous Chemical Reactions Using Explainable… Continue Reading →
Our paper “Explaining the Decisions of Deep Policy Networks for Robotic Manipulations” written by Seongun Kim and Jaesik Choi is accepted to be presented at 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)!
Our paper “Semi-Supervised Training of Deep Generative Models for High-Dimensional Anomaly Detection” written by Qin Xie, Peng Zhang, Boseon Yu, Jaesik Choi is accepted to be published at IEEE Transactions on Neural Networks and Learning Systems!
Our work, Automatic Correction of Internal Units in Generative Neural Networks written by Ali Tousi, Haedong Jeong, Jiyeon Han, Hwanil Choi and Jaesik Choi, presented in CVPR-21 is appeared in the media. Korea: KAIST ‘AI 실수’ 바로잡는 수리기술 개발 http://www.aitimes.kr/news/articleView.html?idxno=21465… Continue Reading →
Our lab is hosting with the PHM Asia Pacific Data Challenge (Chair: Prof. Jaesik Choi, Supporting Staff: Seijun Chung and Seongyeop Jeong) with colleagues. Please consider participating the carefully prepared competition! Data Challenge
Our paper, Interpreting Internal Activation Patterns in Deep Temporal Neural Networks by Finding Prototypes, written by Sohee Cho, Wonjoon Chang, Ginkyeng Lee and Jaesik Choi is accepted at KDD-2021.
Our paper, Learning Basis Functions of Deep Predictive Neural Networks with Global Spatio-Temporal Covariance Loss, written by Boseon Yoo, Jiwoo Lee, Janghoon Ju, Seijun Chung, Soyeon Kim and Jaesik Choi is accepted at ICML-2021.
