4 citations · 10 across the 15 of their papers we have counts for
5 papers · 1 filter
Reward-guided Fine-Tuning of One-Step Generative Models via Wasserstein Gradient Flow
Hoseong Hwang, Woorim Han, Joungin Chun +2
To mitigate the time complexity of generative models, one-step generative models have recently emerged through direct mapping from noise to data in a single forward pass. However,…
Co-occurring Associated REtained concepts in Diffusion Unlearning
Miso Kim, Georu Lee, Yunji Kim +3
Unlearning has emerged as a key technique to mitigate harmful content generation in diffusion models. However, existing methods often remove not only the target concept, but also b…
Machine Unlearning for Masked Diffusion Language Models
Georu Lee, Seungwon Jeong, Hoki Kim +2
Recent masked diffusion language models (MDLMs), such as LLaDA and Dream, have achieved performance comparable to autoregressive large language models. Unlike autoregressive models…
Unlearning for One-Step Generative Models via Unbalanced Optimal Transport
Hyundo Choi, Junhyeong An, Jinseong Park +1
Recent advances in one-step generative frameworks, such as flow map models, have significantly improved the efficiency of image generation by learning direct noise-to-data mappings…
Dementia-R1: Reinforced Pretraining and Reasoning from Unstructured Clinical Notes for Real-World Dementia Prognosis
Choonghan Kim, Hyunmin Hwang, Hangeol Chang +4
While Large Language Models (LLMs) have shown strong performance on clinical text understanding, they struggle with longitudinal prediction tasks such as dementia prognosis, which…