66 citations · 112 across the 4 of their papers we have counts for
4 papers
Automated Learning Rate Scheduler for Large-batch Training
Chiheon Kim, Saehoon Kim, Jongmin Kim +2
Large-batch training has been essential in leveraging large-scale datasets and models in deep learning. While it is computationally beneficial to use large batch sizes, it often re…
Edge-labeling Graph Neural Network for Few-shot Learning
Jongmin Kim, Taesup Kim, Sungwoong Kim +1
In this paper, we propose a novel edge-labeling graph neural network (EGNN), which adapts a deep neural network on the edge-labeling graph, for few-shot learning. The previous grap…
Belief propagation for joint sparse recovery
Jongmin Kim, Woohyuk Chang, Bangchul Jung +2
Compressed sensing (CS) demonstrates that sparse signals can be recovered from underdetermined linear measurements. We focus on the joint sparse recovery problem where multiple sig…
Compressive MUSIC with optimized partial support for joint sparse recovery
Jong Min Kim, Ok Kyun Lee, Jong Chul Ye
Multiple measurement vector (MMV) problem addresses the identification of unknown input vectors that share common sparse support. The MMV problems had been traditionally addressed…