5 citations · 9 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 1 cited
QMP: Q-switch Mixture of Policies for Multi-Task Behavior Sharing
Grace Zhang, Ayush Jain, Injune Hwang +2
Multi-task reinforcement learning (MTRL) aims to learn several tasks simultaneously for better sample efficiency than learning them separately. Traditional methods achieve this by…
cs.LG2020★ 3 cited
Message Passing Adaptive Resonance Theory for Online Active Semi-supervised Learning
Taehyeong Kim, Injune Hwang, Hyundo Lee +4
Active learning is widely used to reduce labeling effort and training time by repeatedly querying only the most beneficial samples from unlabeled data. In real-world problems where…
cs.CV2020★ 5 cited
Label Propagation Adaptive Resonance Theory for Semi-supervised Continuous Learning
Taehyeong Kim, Injune Hwang, Gi-Cheon Kang +3
Semi-supervised learning and continuous learning are fundamental paradigms for human-level intelligence. To deal with real-world problems where labels are rarely given and the oppo…