activity
20182021
most citedLearning What and Where to Transfer

25 citations · 34 across the 3 of their papers we have counts for

collaborators

6 papers

cs.LG20212 cited

Self-Improved Retrosynthetic Planning

Junsu Kim, Sungsoo Ahn, Hankook Lee +1

Retrosynthetic planning is a fundamental problem in chemistry for finding a pathway of reactions to synthesize a target molecule. Recently, search algorithms have shown promising r…

cs.LG20217 cited

RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning

Hankook Lee, Sungsoo Ahn, Seung-Woo Seo +4

Retrosynthesis, of which the goal is to find a set of reactants for synthesizing a target product, is an emerging research area of deep learning. While the existing approaches have…

q-bio.QM2020

Guiding Deep Molecular Optimization with Genetic Exploration

Sungsoo Ahn, Junsu Kim, Hankook Lee +1

De novo molecular design attempts to search over the chemical space for molecules with the desired property. Recently, deep learning has gained considerable attention as a promisin…

cs.LG2019

Self-supervised Label Augmentation via Input Transformations

Hankook Lee, Sung Ju Hwang, Jinwoo Shin

Self-supervised learning, which learns by constructing artificial labels given only the input signals, has recently gained considerable attention for learning representations with…

cs.LG201925 cited

Learning What and Where to Transfer

Yunhun Jang, Hankook Lee, Sung Ju Hwang +1

As the application of deep learning has expanded to real-world problems with insufficient volume of training data, transfer learning recently has gained much attention as means of…

cs.LG2018

Anytime Neural Prediction via Slicing Networks Vertically

Hankook Lee, Jinwoo Shin

The pioneer deep neural networks (DNNs) have emerged to be deeper or wider for improving their accuracy in various applications of artificial intelligence. However, DNNs are often…