ICLR-20 7편 논문 채택

KAIST AI대학원 소속 연구자들의 논문 7편이 ICLR-20 (the International Conference on Learning Representations 2020) 학회에 채택되었습니다.
ICLR는 세계 최고의 기계학습 학회 중의 하나로, 2020년 4월 26일부터 30일까지 에티오피아 Addis Ababa의 Millennium Hall에서 개최될 예정입니다.

채택된 논문들의 목록은 다음과 같습니다.

“Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks”(Oral presentation)
Donghyun Na, Hae Beom Lee, Hayeon Lee, Saehoon Kim, Minseop Park, Eunho Yang (KAIST AI), Sung Ju Hwang (KAIST AI)

“NeurQuRI: Neural Question Requirement Inspector for Answerability Prediction in Machine Reading Comprehension”
Seohyun Back, Sai Chetan Chinthakindi, Akhil Kedia, Haejun Lee, and Jaegul Choo (KAIST AI, as of 3/1/2020)

“Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning”
Kimin Lee, Kibok Lee, Jinwoo Shin (KAIST AI), Honglak Lee

“Lookahead: A Far-sighted Alternative of Magnitude-based Pruning”
Sejun Park*, Jaeho Lee*, Sangwoo Mo and Jinwoo Shin (KAIST AI)

“Meta Dropout: Learning to Perturb Latent Features for Generalization”
Hae Beom Lee, Taewook Nam, Eunho Yang (KAIST AI) and Sung Ju Hwang (KAIST AI)

“Scalable and Order-robust Continual Learning with Additive Parameter Decomposition
Jaehong Yoon, Saehoon Kim, Eunho Yang (KAIST AI) and Sung Ju Hwang (KAIST AI)

“Why Not to Use Zero Imputation? Correcting Sparsity Bias in Training Neural Networks”
Joonyoung Yi, Juhyuk Lee, Sung Ju Hwang (KAIST AI) and Eunho Yang (KAIST AI)