5 citations · 5 across the 1 of their papers we have counts for
3 papers
cs.LG2020
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…
cs.CL2018
GLAC Net: GLocal Attention Cascading Networks for Multi-image Cued Story Generation
Taehyeong Kim, Min-Oh Heo, Seonil Son +2
The task of multi-image cued story generation, such as visual storytelling dataset (VIST) challenge, is to compose multiple coherent sentences from a given sequence of images. The…