no code implementations • 8 Apr 2024 • Seungyub Han, Yeongmo Kim, Taehyun Cho, Jungwoo Lee
One of the objectives of continual learning is to prevent catastrophic forgetting in learning multiple tasks sequentially, and the existing solutions have been driven by the conceptualization of the plasticity-stability dilemma.
1 code implementation • NeurIPS 2023 • Dohyeok Lee, Seungyub Han, Taehyun Cho, Jungwoo Lee
Alleviating overestimation bias is a critical challenge for deep reinforcement learning to achieve successful performance on more complex tasks or offline datasets containing out-of-distribution data.
no code implementations • NeurIPS 2023 • Taehyun Cho, Seungyub Han, Heesoo Lee, Kyungjae Lee, Jungwoo Lee
Distributional reinforcement learning algorithms have attempted to utilize estimated uncertainty for exploration, such as optimism in the face of uncertainty.
Distributional Reinforcement Learning reinforcement-learning
no code implementations • 13 May 2023 • Seungyub Han, Yeongmo Kim, Seokhyeon Ha, Jungwoo Lee, Seunghong Choi
We propose a fine-tuning algorithm for brain tumor segmentation that needs only a few data samples and helps networks not to forget the original tasks.