Multi-segment preserving sampling for deep manifold sampler. - Daniel Berenberg; Jae Hyeon Lee; Simon Kelow; Ji Won Park; Andrew M. Watkins; Vladimir Gligorijevic; Richard Bonneau; Stephen Ra; Kyunghyun Cho (CoRR, 2022)

SupSiam: Non-contrastive Auxiliary Loss for Learning from Molecular Conformers. - Michael Maser; Ji Won Park; Joshua Yao-Yu Lin; Jae Hyeon Lee; Nathan C. Frey; Andrew M. Watkins (CoRR, 2023)

Trap-Based Pest Counting: Multiscale and Deformable Attention CenterNet Integrating Internal LR and HR Joint Feature Learning. - Jae Hyeon Lee; Chang-Hwan Son (CoRR, 2023)

MoleCLUEs: Optimizing Molecular Conformers by Minimization of Differentiable Uncertainty. - Michael Maser; Natasa Tagasovska; Jae Hyeon Lee; Andrew M. Watkins (CoRR, 2023)

Surface ID: a geometry-aware system for protein molecular surface comparison. - Saleh Riahi; Jae Hyeon Lee; Taylor Sorenson; Shuai Wei; Sven Jager; Reza Olfati-Saber; Yanfeng Zhou; Anna Park; Maria Wendt; Hervé Minoux; Yu Qiu (Bioinform., 2023)

Blind Biological Sequence Denoising with Self-Supervised Set Learning. - Nathan H. Ng; Ji Won Park; Jae Hyeon Lee; Ryan Lewis Kelly; Stephen Ra; Kyunghyun Cho (Trans. Mach. Learn. Res., 2024)

Conformation-Aware Structure Prediction of Antigen-Recognizing Immune Proteins. - Frédéric A. Dreyer; Jan Ludwiczak; Karolis Martinkus; Brennan Abanades; Robert G. Alberstein; Pan Kessel; Pranav Rao; Jae Hyeon Lee; Richard Bonneau; Andrew M. Watkins; Franziska Seeger (CoRR, 2025)