54 citations · 160 across the 13 of their papers we have counts for
13 papers
Enhancing Multiple Reliability Measures via Nuisance-extended Information Bottleneck
Jongheon Jeong, Sihyun Yu, Hankook Lee +1
In practical scenarios where training data is limited, many predictive signals in the data can be rather from some biases in data acquisition (i.e., less generalizable), so that on…
Guiding Energy-based Models via Contrastive Latent Variables
Hankook Lee, Jongheon Jeong, Sejun Park +1
An energy-based model (EBM) is a popular generative framework that offers both explicit density and architectural flexibility, but training them is difficult since it is often unst…
Unsupervised Meta-Learning via Few-shot Pseudo-supervised Contrastive Learning
Huiwon Jang, Hankook Lee, Jinwoo Shin
Unsupervised meta-learning aims to learn generalizable knowledge across a distribution of tasks constructed from unlabeled data. Here, the main challenge is how to construct divers…
STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables
Jaehyun Nam, Jihoon Tack, Kyungmin Lee +2
Learning with few labeled tabular samples is often an essential requirement for industrial machine learning applications as varieties of tabular data suffer from high annotation co…
Meta-Learning with Self-Improving Momentum Target
Jihoon Tack, Jongjin Park, Hankook Lee +2
The idea of using a separately trained target model (or teacher) to improve the performance of the student model has been increasingly popular in various machine learning domains,…
Patch-level Representation Learning for Self-supervised Vision Transformers
Sukmin Yun, Hankook Lee, Jaehyung Kim +1
Recent self-supervised learning (SSL) methods have shown impressive results in learning visual representations from unlabeled images. This paper aims to improve their performance f…
Improving Transferability of Representations via Augmentation-Aware Self-Supervision
Hankook Lee, Kibok Lee, Kimin Lee +2
Recent unsupervised representation learning methods have shown to be effective in a range of vision tasks by learning representations invariant to data augmentations such as random…
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…
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…
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…
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…
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…
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…