most citedGlobal Context-aware Representation Learning for Spatially Resolved Transcriptomics

1 citations · 1 across the 5 of their papers we have counts for

collaborators

6 papers

cs.IR2026

PULSE: Socially-Aware User Representation Modeling Toward Parameter-Efficient Graph Collaborative Filtering

Doyun Choi, Cheonwoo Lee, Biniyam Aschalew Tolera +3

Graph-based social recommendation (SocialRec) has emerged as a powerful extension of graph collaborative filtering (GCF), which leverages graph neural networks (GNNs) to capture mu…

cs.LG2025

Oldie but Goodie: Re-illuminating Label Propagation on Graphs with Partially Observed Features

Sukwon Yun, Xin Liu, Yunhak Oh +4

In real-world graphs, we often encounter missing feature situations where a few or the majority of node features, e.g., sensitive information, are missed. In such scenarios, direct…

cs.LG20251 cited

Global Context-aware Representation Learning for Spatially Resolved Transcriptomics

Yunhak Oh, Junseok Lee, Yeongmin Kim +3

Spatially Resolved Transcriptomics (SRT) is a cutting-edge technique that captures the spatial context of cells within tissues, enabling the study of complex biological networks. R…

cs.AI2025

Subtle Risks, Critical Failures: A Framework for Diagnosing Physical Safety of LLMs for Embodied Decision Making

Yejin Son, Minseo Kim, Sungwoong Kim +5

Large Language Models (LLMs) are increasingly used for decision making in embodied agents, yet existing safety evaluations often rely on coarse success rates and domain-specific se…

cs.LG2025

Subgraph Federated Learning for Local Generalization

Sungwon Kim, Yoonho Lee, Yunhak Oh +6

Federated Learning (FL) on graphs enables collaborative model training to enhance performance without compromising the privacy of each client. However, existing methods often overl…

cs.LG2025

MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations

Namwoo Kim, Takahiro Yabe, Chanyoung Park +1

Recently, learning effective representations of urban regions has gained significant attention as a key approach to understanding urban dynamics and advancing smarter cities. Exist…