activity
20192022
most citedBHIN2vec: Balancing the Type of Relation in Heterogeneous Information Network

20 citations · 22 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV20221 cited

Kinematic-aware Hierarchical Attention Network for Human Pose Estimation in Videos

Kyung-Min Jin, Byoung-Sung Lim, Gun-Hee Lee +2

Previous video-based human pose estimation methods have shown promising results by leveraging aggregated features of consecutive frames. However, most approaches compromise accurac…

cs.CL2022

Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generation

Dongha Lee, Jiaming Shen, Seonghyeon Lee +3

Topic taxonomies display hierarchical topic structures of a text corpus and provide topical knowledge to enhance various NLP applications. To dynamically incorporate new topic info…

cs.AI2022

Toward Interpretable Semantic Textual Similarity via Optimal Transport-based Contrastive Sentence Learning

Seonghyeon Lee, Dongha Lee, Seongbo Jang +1

Recently, finetuning a pretrained language model to capture the similarity between sentence embeddings has shown the state-of-the-art performance on the semantic textual similarity…

cs.CL2021

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…

cs.LG20211 cited

Learnable Dynamic Temporal Pooling for Time Series Classification

Dongha Lee, Seonghyeon Lee, Hwanjo Yu

With the increase of available time series data, predicting their class labels has been one of the most important challenges in a wide range of disciplines. Recent studies on time…

cs.SI201920 cited

BHIN2vec: Balancing the Type of Relation in Heterogeneous Information Network

Seonghyeon Lee, Chanyoung Park, Hwanjo Yu

The goal of network embedding is to transform nodes in a network to a low-dimensional embedding vectors. Recently, heterogeneous network has shown to be effective in representing d…