8 citations · 9 across the 7 of their papers we have counts for
7 papers
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…
Geometrically Aligned Transfer Encoder for Inductive Transfer in Regression Tasks
Sung Moon Ko, Sumin Lee, Dae-Woong Jeong +2
Transfer learning is a crucial technique for handling a small amount of data that is potentially related to other abundant data. However, most of the existing methods are focused o…
3D Denoisers are Good 2D Teachers: Molecular Pretraining via Denoising and Cross-Modal Distillation
Sungjun Cho, Dae-Woong Jeong, Sung Moon Ko +5
Pretraining molecular representations from large unlabeled data is essential for molecular property prediction due to the high cost of obtaining ground-truth labels. While there ex…
Grouping-matrix based Graph Pooling with Adaptive Number of Clusters
Sung Moon Ko, Sungjun Cho, Dae-Woong Jeong +3
Graph pooling is a crucial operation for encoding hierarchical structures within graphs. Most existing graph pooling approaches formulate the problem as a node clustering task whic…