Federated Continual Learning with Weighted Inter-client Transfer

ICML 2021 Federated Continual Learning with Weighted Inter-client Transfer Jaehong Yoon, Wonyong Jeong, Giwoong Lee, Eunho Yang and Sung Ju Hwang  There has been a surge of interest in continual learning and federated learning, both of which are important in deep neural networks ...

Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning

NeurIPS 2020 Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning Jaehyung Kim, Youngbum Hur, Sejun Park, Eunho Yang, Sung Ju Hwang, Jinwoo Shin  While semi-supervised learning (SSL) has proven to be a promising way for leveraging unlabeled ...

Deep Mixed Effect Models using Gaussian Process: A Personalized and Reliable Prediction Model for Healthcare

AAAI 2020 Deep Mixed Effect Models using Gaussian Process: A Personalized and Reliable Prediction Model for Healthcare Ingyo Chung, Saehoon Kim, Juho Lee, Kwang Joon Kim, Sung Ju Hwang, Eunho Yang  We present a personalized and reliable prediction model for healthcare, which can ...

Cost-effective Interactive Attention Learning with Neural Attention Processes

ICML 2020 Cost-effective Interactive Attention with Neural Attention Processes Jay Heo, Junhyeon Park, Hyewon Jeong, Kwang Joon Kim, Juho Lee, Eunho Yang, Sung Ju Hwang  We propose a novel interactive learning framework which we refer to as Interactive Attention Learning (IA...

Comprehensive Study on Molecular Supervised Learning with Graph Neural Networks

ACS 2020 Comprehensive Study on Molecular Supervised Learning with Graph Neural Networks Doyeong Hwang, Soojung Yang, Yongchan Kwon, Kyung Hoon Lee, Grace Lee, Hanseok Jo, Seyeol Yoon, Seongok Ryu  This work considers strategies to develop accurate and reliable gra...

Bootstrapping Neural Processes

NeurIPS 2020 Bootstrapping Neural Processes Juho Lee, Yoonho Lee, Jungtaek Kim, Eunho Yang, Sung Ju Hwang, Yee Whye Teh  Unlike in the traditional statistical modeling for which a user typically hand-specify a prior, Neural Processes (NPs) implicitly define a broad class of stoch...

Attribution Preservation in Network Compression for Reliable Network Interpretation

NeurIPS 2020 Attribution Preservation in Network Compression for Reliable Network Interpretation Geondo Park, June Yong Yang, Sung Ju Hwang, Eunho Yang  Neural networks embedded in safety-sensitive applications such as self-driving cars and wearable health monitors rely...

Adversarial Self-Supervised Contrastive Learning

NeurIPS 2020 Adversarial Self-Supervised Contrastive Learning Minseon Kim, Jihoon Tack, Sung Ju Hwang  Existing adversarial learning approaches mostly use class labels to generate adversarial samples that lead to incorrect predictions, which are then used to augment the training ...

Adversarial Neural Pruning with Latent Vulnerability Suppression

ICML 2020 Adversarial Neural Pruning with Latent Vulnerability Suppression Divyam Madaan, Jinwoo Shin, Sung Ju Hwang  Despite the remarkable performance of deep neural networks on various computer vision tasks, they are known to be susceptible to adversarial perturbations, w...

A benchmark study on reliable molecular supervised learning via Bayesian learning

ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning A benchmark study on reliable molecularsupervised learning via Bayesian learning Doyeong Hwang, Grace Lee, Hanseok Jo, Seyoul Yoon, Seongok Ryu  Virtual screening aims to find desirable compounds from chemical libr...