A paper is accepted at ICAIF-2020. Youngjin Park, Deokjun Eom, and Jaesik Choi, Improved Predictive Deep Temporal Neural Networks with Trend Filtering, ICAIF-2020
KAIST AI대학원 확률형 인공지는 연구실에서 수행하는 연구(시계열 데이터 분석 및 설명가능 인공지능)에 관심있는 지원자는 이력서(CV)를 email@example.com로 지원 부탁드립니다. 관심있는 지원자를 위해서 다음과 같은 FAQ를 준비했습니다. https://docs.google.com/document/d/1Y7nZnkSMRI4uB9WJTTsmCyR-mVM3Z4plQ9_wJlYMadY/edit?usp=sharing If you are interested in research (time series analysis and explainable AI) at SAIL@KAIST,… Continue Reading →
A tutorial proposed to KDD 2020, Interpreting and Explaining Deep Neural Networks: A Perspective on Time Series Data, is accepted.
A papers is accepted at ICRA-2020. YongHyeok Seo, Dongju Shin, Jaesik Choi, and Se Young Chun, A Single Multi-Task Deep Neural Network with Post-Processing for Object Detection with Reasoning and Robotic Grasp Detection, ICRA-2020 (pdf, video).
A perspective, review article, XAI – Explainable artificial intelligence, co-authored by Prof. Jaesik Choi with David Gunning (DARPA, currently at Facebook) and other researchers is published at Science Robotics.
Anh Tong, a PhD student of UNIST, received NAVER PhD Fellowship award.
Two papers are accepted at AAAI-2020. Giyoung Jeon*, Haedong Jeong* and Jaesik Choi, An Efficient Explorative Sampling Considering the Generative Boundaries of Deep Generative Neural Networks, AAAI-2020 (Accepted, Oral, * contributed equally). Woojeong Nam, Shir Gur, Jaesik Choi, Lior Wolf… Continue Reading →
Our research team (http://iaic.unist.ac.kr) successfully has finished a research project with Korea East-West Power Company to build time series deep learning models which diagnose tubes in Power Plant Boilers of the Dangjin power station. Our AI module and software are… Continue Reading →
Our paper, Markov Information Bottleneck to Improve Information Flow in Stochastic Neural Networks, written by Thanh and Jaesik is accepted at Special Issue “Information–Theoretic Approaches to Computational Intelligence” of Entropy. https://www.mdpi.com/journal/entropy/special_issues/information_theoretic_computational_intelligence