38 citations · 52 across the 3 of their papers we have counts for
3 papers
cs.IR2021
An Efficient Combinatorial Optimization Model Using Learning-to-Rank Distillation
Honguk Woo, Hyunsung Lee, Sangwoo Cho
Recently, deep reinforcement learning (RL) has proven its feasibility in solving combinatorial optimization problems (COPs). The learning-to-rank techniques have been studied in th…
cs.LG2021★ 14 cited
Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation
Taehyeon Kim, Jaehoon Oh, NakYil Kim +2
Knowledge distillation (KD), transferring knowledge from a cumbersome teacher model to a lightweight student model, has been investigated to design efficient neural architectures.…
cs.LG2021★ 38 cited
FINE Samples for Learning with Noisy Labels
Taehyeon Kim, Jongwoo Ko, Sangwook Cho +2
Modern deep neural networks (DNNs) become frail when the datasets contain noisy (incorrect) class labels. Robust techniques in the presence of noisy labels can be categorized into…