Anytime Neural Prediction via Slicing Networks Vertically. - Hankook Lee; Jinwoo Shin (CoRR, 2018)

Learning What and Where to Transfer. - Yunhun Jang; Hankook Lee; Sung Ju Hwang; Jinwoo Shin (ICML, 2019)

Rethinking Data Augmentation: Self-Supervision and Self-Distillation. - Hankook Lee; Sung Ju Hwang; Jinwoo Shin (CoRR, 2019)

Self-supervised Label Augmentation via Input Transformations. - Hankook Lee; Sung Ju Hwang; Jinwoo Shin (ICML, 2020)

Guiding Deep Molecular Optimization with Genetic Exploration. - Sungsoo Ahn; Junsu Kim; Hankook Lee; Jinwoo Shin (NeurIPS, 2020)

GTA: Graph Truncated Attention for Retrosynthesis. - Seung-Woo Seo; You Young Song; June Yong Yang; Seohui Bae; Hankook Lee; Jinwoo Shin; Sung Ju Hwang; Eunho Yang (AAAI, 2021)

Self-Improved Retrosynthetic Planning. - Junsu Kim; Sungsoo Ahn; Hankook Lee; Jinwoo Shin (ICML, 2021)

RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning. - Hankook Lee; Sungsoo Ahn; Seung-Woo Seo; You Young Song; Eunho Yang; Sung Ju Hwang; Jinwoo Shin (IJCAI, 2021)

Improving Transferability of Representations via Augmentation-Aware Self-Supervision. - Hankook Lee; Kibok Lee 003; Kimin Lee; Honglak Lee; Jinwoo Shin (NeurIPS, 2021)

Patch-level Representation Learning for Self-supervised Vision Transformers. - Sukmin Yun; Hankook Lee; Jaehyung Kim 001; Jinwoo Shin (CVPR, 2022)

Meta-Learning with Self-Improving Momentum Target. - Jihoon Tack; Jongjin Park; Hankook Lee; Jaeho Lee 001; Jinwoo Shin (NeurIPS, 2022)

Enhancing Multiple Reliability Measures via Nuisance-Extended Information Bottleneck. - Jongheon Jeong; Sihyun Yu; Hankook Lee; Jinwoo Shin (CVPR, 2023)

Unsupervised Meta-learning via Few-shot Pseudo-supervised Contrastive Learning. - Huiwon Jang; Hankook Lee; Jinwoo Shin (ICLR, 2023)

Guiding Energy-based Models via Contrastive Latent Variables. - Hankook Lee; Jongheon Jeong; Sejun Park; Jinwoo Shin (ICLR, 2023)

STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables. - Jaehyun Nam; Jihoon Tack; Kyungmin Lee; Hankook Lee; Jinwoo Shin (ICLR, 2023)

Projection Regret: Reducing Background Bias for Novelty Detection via Diffusion Models. - Sungik Choi; Hankook Lee; Honglak Lee; Moontae Lee (NeurIPS, 2023)

Curve Your Attention: Mixed-Curvature Transformers for Graph Representation Learning. - Sungjun Cho; Seunghyuk Cho; Sungwoo Park; Hankook Lee; Honglak Lee; Moontae Lee (CoRR, 2023)

Learning Equi-Angular Representations for Online Continual Learning. - Minhyuk Seo; Hyunseo Koh; Wonje Jeung; Minjae Lee; San Kim; Hankook Lee; Sungjun Cho; Sungik Choi; Hyunwoo Kim; Jonghyun Choi (CVPR, 2024)

Few-Shot Anomaly Detection via Personalization. - Sangkyung Kwak; Jongheon Jeong; Hankook Lee; Woohyuck Kim; Dongho Seo; Woojin Yun; Wonjin Lee; Jinwoo Shin (IEEE Access, 2024)

Partial-Multivariate Model for Forecasting. - Jaehoon Lee; Hankook Lee; Sungik Choi; Sungjun Cho; Moontae Lee (CoRR, 2024)

Holistic Molecular Representation Learning via Multi-view Fragmentation. - Seojin Kim; Jaehyun Nam; Junsu Kim; Hankook Lee; Sungsoo Ahn; Jinwoo Shin (Trans. Mach. Learn. Res., 2024)

Diffusion-based Semantic Outlier Generation via Nuisance Awareness for Out-of-Distribution Detection. - Suhee Yoon; Sanghyu Yoon; Ye Seul Sim; Sungik Choi; Kyungeun Lee; Hye-Seung Cho; Hankook Lee; Woohyung Lim (AAAI, 2025)

Not All Clients Are Equal: Personalized Federated Learning on Heterogeneous Multi-Modal Clients. - Minhyuk Seo; Taeheon Kim; Hankook Lee; Jonghyun Choi; Tinne Tuytelaars (CoRR, 2025)