148 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Seoul National UniversityKR34 papers
- Sungkyunkwan UniversityKR17 papers
- Yonsei UniversityKR16 papers
- Korea UniversityKR15 papers
- Korea Institute for Advanced StudyKR14 papers
- University of PittsburghUS14 papers
- High Energy Accelerator Research OrganizationJP11 papers
- Kyungpook National UniversityKR11 papers
- Institute for Basic ScienceKR10 papers
- New Generation University CollegeET10 papers
- Tohoku UniversityJP10 papers
- Chonnam National UniversityKR9 papers
9 papers · 1 filter
IgPose: A Generative Data-Augmented Pipeline for Robust Immunoglobulin-Antigen Binding Prediction
Tien-Cuong Bui, Injae Chung, Wonjun Lee +2
Predicting immunoglobulin-antigen (Ig-Ag) binding remains a significant challenge due to the paucity of experimentally-resolved complexes and the limited accuracy of de novo Ig str…
GC-Fed: Gradient Centralized Federated Learning with Partial Client Participation
Jungwon Seo, Ferhat Ozgur Catak, Chunming Rong +2
Federated Learning (FL) enables privacy-preserving multi-source information fusion (MSIF) but is challenged by client drift in highly heterogeneous data settings. Many existing dri…
Role of Mixup in Topological Persistence Based Knowledge Distillation for Wearable Sensor Data
Eun Som Jeon, Hongjun Choi, Matthew P. Buman +1
The analysis of wearable sensor data has enabled many successes in several applications. To represent the high-sampling rate time-series with sufficient detail, the use of topologi…
FedShift: Robust Federated Learning Aggregation Scheme in Resource Constrained Environment via Weight Shifting
Jungwon Seo, Minhoe Kim, Chunming Rong
Federated Learning (FL) commonly relies on a central server to coordinate training across distributed clients. While effective, this paradigm suffers from significant communication…
Explicit Feature Interaction-aware Graph Neural Networks
Minkyu Kim, Hyun-Soo Choi, Jinho Kim
Graph neural networks (GNNs) are powerful tools for handling graph-structured data. However, their design often limits them to learning only higher-order feature interactions, leav…
GeoT: A Geometry-aware Transformer for Reliable Molecular Property Prediction and Chemically Interpretable Representation Learning
Bumju Kwak, Jiwon Park, Taewon Kang +3
In recent years, molecular representation learning has emerged as a key area of focus in various chemical tasks. However, many existing models fail to fully consider the geometric…
Confidence-Aware Learning for Deep Neural Networks
Jooyoung Moon, Jihyo Kim, Younghak Shin +1
Despite the power of deep neural networks for a wide range of tasks, an overconfident prediction issue has limited their practical use in many safety-critical applications. Many re…
Relevance Factor VAE: Learning and Identifying Disentangled Factors
Minyoung Kim, Yuting Wang, Pritish Sahu +1
We propose a novel VAE-based deep auto-encoder model that can learn disentangled latent representations in a fully unsupervised manner, endowed with the ability to identify all mea…
DNA Steganalysis Using Deep Recurrent Neural Networks
Ho Bae, Byunghan Lee, Sunyoung Kwon +1
Recent advances in next-generation sequencing technologies have facilitated the use of deoxyribonucleic acid (DNA) as a novel covert channels in steganography. There are various me…