1 citations · 1 across the 6 of their papers we have counts for
6 papers
Towards a Generalizable AI for Materials Discovery: Validation through Immersion Coolant Screening
Hyunseung Kim, Dae-Woong Jeong, Changyoung Park +11
Artificial intelligence (AI) has emerged as a powerful accelerator of materials discovery, yet most existing models remain problem-specific, requiring additional data collection an…
Robust Multi-Omics Integration from Incomplete Modalities Significantly Improves Prediction of Alzheimer's Disease
Sungjoon Park, Kyungwook Lee, Soorin Yim +8
Multi-omics data capture complex biomolecular interactions and provide insights into metabolism and disease. However, missing modalities hinder integrative analysis across heteroge…
Geometric Embedding Alignment via Curvature Matching in Transfer Learning
Sung Moon Ko, Jaewan Lee, Sumin Lee +3
Geometrical interpretations of deep learning models offer insightful perspectives into their underlying mathematical structures. In this work, we introduce a novel approach that le…
Scalable Multi-Task Transfer Learning for Molecular Property Prediction
Chanhui Lee, Dae-Woong Jeong, Sung Moon Ko +6
Molecules have a number of distinct properties whose importance and application vary. Often, in reality, labels for some properties are hard to achieve despite their practical impo…
Task Addition in Multi-Task Learning by Geometrical Alignment
Soorin Yim, Dae-Woong Jeong, Sung Moon Ko +4
Training deep learning models on limited data while maintaining generalization is one of the fundamental challenges in molecular property prediction. One effective solution is tran…
Multitask Extension of Geometrically Aligned Transfer Encoder
Sung Moon Ko, Sumin Lee, Dae-Woong Jeong +4
Molecular datasets often suffer from a lack of data. It is well-known that gathering data is difficult due to the complexity of experimentation or simulation involved. Here, we lev…