3 citations · 3 across the 10 of their papers we have counts for
11 papers
Are You the A-hole? A Fair, Multi-Perspective Ethical Reasoning Framework
Sheza Munir, Ahanaf Rodoshi, Sumin Lee +3
Standard methods for aggregating natural language judgments, such as majority voting, often fail to produce logically consistent results when applied to high-conflict domains, trea…
I'm Fine, But My Voice Isn't: Cross-Modal Affective Dissonance Detection for Reflective Journaling
Sumin Lee
Digital journaling creates an authenticity gap: users consciously translate raw emotions into text, often sanitizing narratives even in private writing. We formalize this as Cross-…
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…
ORCA: ORchestrating Causal Agent
Joanie Hayoun Chung, Sumin Lee, Sungbin Lim
Causal analysis on relational databases is challenging, as analysis datasets must be repeatedly queried from complex schemas. Recent LLM systems can automate individual steps, but…
Score-informed Neural Operator for Enhancing Ordering-based Causal Discovery
Jiyeon Kang, Songseong Kim, Chanhui Lee +6
Ordering-based approaches to causal discovery identify topological orders of causal graphs, providing scalable alternatives to combinatorial search methods. Under the Additive Nois…
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…
Accurate, reliable and interpretable solubility prediction of druglike molecules with attention pooling and Bayesian learning
Seongok Ryu, Sumin Lee
In drug discovery, aqueous solubility is an important pharmacokinetic property which affects absorption and assay availability of drug. Thus, in silico prediction of solubility has…