80 citations · 237 across the 16 of their papers we have counts for
18 papers
Weakly Supervised Temporal Anomaly Segmentation with Dynamic Time Warping
Dongha Lee, Sehun Yu, Hyunjun Ju +1
Most recent studies on detecting and localizing temporal anomalies have mainly employed deep neural networks to learn the normal patterns of temporal data in an unsupervised manner…
Unsupervised Proxy Selection for Session-based Recommender Systems
Junsu Cho, SeongKu Kang, Dongmin Hyun +1
Session-based Recommender Systems (SRSs) have been actively developed to recommend the next item of an anonymous short item sequence (i.e., session). Unlike sequence-aware recommen…
Topology Distillation for Recommender System
SeongKu Kang, Junyoung Hwang, Wonbin Kweon +1
Recommender Systems (RS) have employed knowledge distillation which is a model compression technique training a compact student model with the knowledge transferred from a pre-trai…
Bidirectional Distillation for Top-K Recommender System
Wonbin Kweon, SeongKu Kang, Hwanjo Yu
Recommender systems (RS) have started to employ knowledge distillation, which is a model compression technique training a compact model (student) with the knowledge transferred fro…
Out-of-Manifold Regularization in Contextual Embedding Space for Text Classification
Seonghyeon Lee, Dongha Lee, Hwanjo Yu
Recent studies on neural networks with pre-trained weights (i.e., BERT) have mainly focused on a low-dimensional subspace, where the embedding vectors computed from input words (or…
Bootstrapping User and Item Representations for One-Class Collaborative Filtering
Dongha Lee, SeongKu Kang, Hyunjun Ju +2
The goal of one-class collaborative filtering (OCCF) is to identify the user-item pairs that are positively-related but have not been interacted yet, where only a small portion of…