64 citations · 115 across the 25 of their papers we have counts for
25 papers
Diffusion-based Episodes Augmentation for Offline Multi-Agent Reinforcement Learning
Jihwan Oh, Sungnyun Kim, Gahee Kim +2
Offline multi-agent reinforcement learning (MARL) is increasingly recognized as crucial for effectively deploying RL algorithms in environments where real-time interaction is impra…
VACoDe: Visual Augmented Contrastive Decoding
Sihyeon Kim, Boryeong Cho, Sangmin Bae +2
Despite the astonishing performance of recent Large Vision-Language Models (LVLMs), these models often generate inaccurate responses. To address this issue, previous studies have f…
BAPO: Base-Anchored Preference Optimization for Overcoming Forgetting in Large Language Models Personalization
Gihun Lee, Minchan Jeong, Yujin Kim +4
While learning to align Large Language Models (LLMs) with human preferences has shown remarkable success, aligning these models to meet the diverse user preferences presents furthe…
Synergistic Integration of Coordinate Network and Tensorial Feature for Improving Neural Radiance Fields from Sparse Inputs
Mingyu Kim, Jun-Seong Kim, Se-Young Yun +1
The multi-plane representation has been highlighted for its fast training and inference across static and dynamic neural radiance fields. This approach constructs relevant features…
FedDr+: Stabilizing Dot-regression with Global Feature Distillation for Federated Learning
Seongyoon Kim, Minchan Jeong, Sungnyun Kim +3
Federated Learning (FL) has emerged as a pivotal framework for the development of effective global models (global FL) or personalized models (personalized FL) across clients with h…
Bayesian Multi-Task Transfer Learning for Soft Prompt Tuning
Haeju Lee, Minchan Jeong, Se-Young Yun +1
Prompt tuning, in which prompts are optimized to adapt large-scale pre-trained language models to downstream tasks instead of fine-tuning the full model parameters, has been shown…