3 citations · 7 across the 6 of their papers we have counts for
5 papers · 1 filter
Continuous Diffusion Model for Language Modeling
Jaehyeong Jo, Sung Ju Hwang
Diffusion models have emerged as a promising alternative to autoregressive models in modeling discrete categorical data. However, diffusion models that directly work on discrete da…
Generative Modeling on Manifolds Through Mixture of Riemannian Diffusion Processes
Jaehyeong Jo, Sung Ju Hwang
Learning the distribution of data on Riemannian manifolds is crucial for modeling data from non-Euclidean space, which is required by many applications in diverse scientific fields…
Self-Supervised Dataset Distillation for Transfer Learning
Dong Bok Lee, Seanie Lee, Joonho Ko +3
Dataset distillation methods have achieved remarkable success in distilling a large dataset into a small set of representative samples. However, they are not designed to produce a…
Drug Discovery with Dynamic Goal-aware Fragments
Seul Lee, Seanie Lee, Kenji Kawaguchi +1
Fragment-based drug discovery is an effective strategy for discovering drug candidates in the vast chemical space, and has been widely employed in molecular generative models. Howe…
DiffusionNAG: Predictor-guided Neural Architecture Generation with Diffusion Models
Sohyun An, Hayeon Lee, Jaehyeong Jo +2
Existing NAS methods suffer from either an excessive amount of time for repetitive sampling and training of many task-irrelevant architectures. To tackle such limitations of existi…