16 citations · 33 across the 5 of their papers we have counts for
5 papers
Refine Myself by Teaching Myself: Feature Refinement via Self-Knowledge Distillation
Mingi Ji, Seungjae Shin, Seunghyun Hwang +2
Knowledge distillation is a method of transferring the knowledge from a pretrained complex teacher model to a student model, so a smaller network can replace a large teacher networ…
Show, Attend and Distill:Knowledge Distillation via Attention-based Feature Matching
Mingi Ji, Byeongho Heo, Sungrae Park
Knowledge distillation extracts general knowledge from a pre-trained teacher network and provides guidance to a target student network. Most studies manually tie intermediate featu…
Sequential Recommendation with Relation-Aware Kernelized Self-Attention
Mingi Ji, Weonyoung Joo, Kyungwoo Song +2
Recent studies identified that sequential Recommendation is improved by the attention mechanism. By following this development, we propose Relation-Aware Kernelized Self-Attention…
Hierarchical Context enabled Recurrent Neural Network for Recommendation
Kyungwoo Song, Mingi Ji, Sungrae Park +1
A long user history inevitably reflects the transitions of personal interests over time. The analyses on the user history require the robust sequential model to anticipate the tran…
Adversarial Dropout for Recurrent Neural Networks
Sungrae Park, Kyungwoo Song, Mingi Ji +2
Successful application processing sequential data, such as text and speech, requires an improved generalization performance of recurrent neural networks (RNNs). Dropout techniques…