22 citations · 40 across the 22 of their papers we have counts for
6 papers · 1 filter
Physics Informed Distillation for Diffusion Models
Joshua Tian Jin Tee, Kang Zhang, Hee Suk Yoon +3
Diffusion models have recently emerged as a potent tool in generative modeling. However, their inherent iterative nature often results in sluggish image generation due to the requi…
BI-MDRG: Bridging Image History in Multimodal Dialogue Response Generation
Hee Suk Yoon, Eunseop Yoon, Joshua Tian Jin Tee +4
Multimodal Dialogue Response Generation (MDRG) is a recently proposed task where the model needs to generate responses in texts, images, or a blend of both based on the dialogue co…
LI-TTA: Language Informed Test-Time Adaptation for Automatic Speech Recognition
Eunseop Yoon, Hee Suk Yoon, John Harvill +2
Test-Time Adaptation (TTA) has emerged as a crucial solution to the domain shift challenge, wherein the target environment diverges from the original training environment. A prime…
TLCR: Token-Level Continuous Reward for Fine-grained Reinforcement Learning from Human Feedback
Eunseop Yoon, Hee Suk Yoon, SooHwan Eom +7
Reinforcement Learning from Human Feedback (RLHF) leverages human preference data to train language models to align more closely with human essence. These human preference data, ho…
C-TPT: Calibrated Test-Time Prompt Tuning for Vision-Language Models via Text Feature Dispersion
Hee Suk Yoon, Eunseop Yoon, Joshua Tian Jin Tee +3
In deep learning, test-time adaptation has gained attention as a method for model fine-tuning without the need for labeled data. A prime exemplification is the recently proposed te…
AdaMER-CTC: Connectionist Temporal Classification with Adaptive Maximum Entropy Regularization for Automatic Speech Recognition
SooHwan Eom, Eunseop Yoon, Hee Suk Yoon +3
In Automatic Speech Recognition (ASR) systems, a recurring obstacle is the generation of narrowly focused output distributions. This phenomenon emerges as a side effect of Connecti…