2 citations · 6 across the 12 of their papers we have counts for
6 papers · 1 filter
Bridging the Gap Between Molecule and Textual Descriptions via Substructure-aware Alignment
Hyuntae Park, Yeachan Kim, SangKeun Lee
Molecule and text representation learning has gained increasing interest due to its potential for enhancing the understanding of chemical information. However, existing models ofte…
C2A: Client-Customized Adaptation for Parameter-Efficient Federated Learning
Yeachan Kim, Junho Kim, Wing-Lam Mok +2
Despite the versatility of pre-trained language models (PLMs) across domains, their large memory footprints pose significant challenges in federated learning (FL), where the traini…
CleaR: Towards Robust and Generalized Parameter-Efficient Fine-Tuning for Noisy Label Learning
Yeachan Kim, Junho Kim, SangKeun Lee
Parameter-efficient fine-tuning (PEFT) has enabled the efficient optimization of cumbersome language models in real-world settings. However, as datasets in such environments often…
Learning From Drift: Federated Learning on Non-IID Data via Drift Regularization
Yeachan Kim, Bonggun Shin
Federated learning algorithms perform reasonably well on independent and identically distributed (IID) data. They, on the other hand, suffer greatly from heterogeneous environments…
Phase-shifted Adversarial Training
Yeachan Kim, Seongyeon Kim, Ihyeok Seo +1
Adversarial training has been considered an imperative component for safely deploying neural network-based applications to the real world. To achieve stronger robustness, existing…
An Interpretable Framework for Drug-Target Interaction with Gated Cross Attention
Yeachan Kim, Bonggun Shin
In silico prediction of drug-target interactions (DTI) is significant for drug discovery because it can largely reduce timelines and costs in the drug development process. Specific…