377 citations · 392 across the 13 of their papers we have counts for
9 papers · 1 filter
FLoRA: Fused forward-backward adapters for parameter efficient fine-tuning and reducing inference-time latencies of LLMs
Dhananjaya Gowda, Seoha Song, Junhyun Lee +1
As the large language models (LLMs) grow in size each day, efficient training and fine-tuning has never been as important as nowadays. This resulted in the great interest in parame…
Understanding and Tackling Over-Dilution in Graph Neural Networks
Junhyun Lee, Veronika Thost, Bumsoo Kim +2
Message Passing Neural Networks (MPNNs) hold a key position in machine learning on graphs, but they struggle with unintended behaviors, such as over-smoothing and over-squashing, d…
TurboHopp: Accelerated Molecule Scaffold Hopping with Consistency Models
Kiwoong Yoo, Owen Oertell, Junhyun Lee +2
Navigating the vast chemical space of druggable compounds is a formidable challenge in drug discovery, where generative models are increasingly employed to identify viable candidat…
CRADLE-VAE: Enhancing Single-Cell Gene Perturbation Modeling with Counterfactual Reasoning-based Artifact Disentanglement
Seungheun Baek, Soyon Park, Yan Ting Chok +4
Predicting cellular responses to various perturbations is a critical focus in drug discovery and personalized therapeutics, with deep learning models playing a significant role in…
Subgraph-level Universal Prompt Tuning
Junhyun Lee, Wooseong Yang, Jaewoo Kang
In the evolving landscape of machine learning, the adaptation of pre-trained models through prompt tuning has become increasingly prominent. This trend is particularly observable i…
MolPLA: A Molecular Pretraining Framework for Learning Cores, R-Groups and their Linker Joints
Mogan Gim, Jueon Park, Soyon Park +5
Molecular core structures and R-groups are essential concepts in drug development. Integration of these concepts with conventional graph pre-training approaches can promote deeper…