1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 1 cited
On the Convergence of Continual Learning with Adaptive Methods
Seungyub Han, Yeongmo Kim, Taehyun Cho +1
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 concep…
cs.LG2024
SPQR: Controlling Q-ensemble Independence with Spiked Random Model for Reinforcement Learning
Dohyeok Lee, Seungyub Han, Taehyun Cho +1
Alleviating overestimation bias is a critical challenge for deep reinforcement learning to achieve successful performance on more complex tasks or offline datasets containing out-o…
eess.IV2023
Learning to Learn Unlearned Feature for Brain Tumor Segmentation
Seungyub Han, Yeongmo Kim, Seokhyeon Ha +2
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. Our approach is based on act…