12 citations · 12 across the 10 of their papers we have counts for
Showing 2025Show all
3 papers · 1 filter
cs.LG2025
Dynamic Mixture of Experts Against Severe Distribution Shifts
Donghu Kim
The challenge of building neural networks that can continuously learn and adapt to evolving data streams is central to the fields of continual learning (CL) and reinforcement learn…
cs.CL2025
Building Resource-Constrained Language Agents: A Korean Case Study on Chemical Toxicity Information
Hojun Cho, Donghu Kim, Soyoung Yang +3
Language agents powered by large language models (LLMs) face significant deployment challenges in resource-constrained environments, particularly for specialized domains and less-c…
cs.LG2025
Hyperspherical Normalization for Scalable Deep Reinforcement Learning
Hojoon Lee, Youngdo Lee, Takuma Seno +3
Scaling up the model size and computation has brought consistent performance improvements in supervised learning. However, this lesson often fails to apply to reinforcement learnin…