54 citations · 90 across the 13 of their papers we have counts for
13 papers
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…
Boosting Convolutional Neural Networks' Protein Binding Site Prediction Capacity Using SE(3)-invariant transformers, Transfer Learning and Homology-based Augmentation
Daeseok Lee, Jeunghyun Byun, Bonggun Shin
Figuring out small molecule binding sites in target proteins, in the resolution of either pocket or residue, is critical in many virtual and real drug-discovery scenarios. Since it…
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…
Improving group robustness under noisy labels using predictive uncertainty
Dongpin Oh, Dae Lee, Jeunghyun Byun +1
The standard empirical risk minimization (ERM) can underperform on certain minority groups (i.e., waterbirds in lands or landbirds in water) due to the spurious correlation between…
In Defense of Core-set: A Density-aware Core-set Selection for Active Learning
Yeachan Kim, Bonggun Shin
Active learning enables the efficient construction of a labeled dataset by labeling informative samples from an unlabeled dataset. In a real-world active learning scenario, conside…
Improving evidential deep learning via multi-task learning
Dongpin Oh, Bonggun Shin
The Evidential regression network (ENet) estimates a continuous target and its predictive uncertainty without costly Bayesian model averaging. However, it is possible that the targ…
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…
Controlled Molecule Generator for Optimizing Multiple Chemical Properties
Bonggun Shin, Sungsoo Park, JinYeong Bak +1
Generating a novel and optimized molecule with desired chemical properties is an essential part of the drug discovery process. Failure to meet one of the required properties can fr…
Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction
Bonggun Shin, Sungsoo Park, Keunsoo Kang +1
Predicting drug-target interactions (DTI) is an essential part of the drug discovery process, which is an expensive process in terms of time and cost. Therefore, reducing DTI cost…
Multimodal Ensemble Approach to Incorporate Various Types of Clinical Notes for Predicting Readmission
Bonggun Shin, Julien Hogan, Andrew B. Adams +3
Electronic Health Records (EHRs) have been heavily used to predict various downstream clinical tasks such as readmission or mortality. One of the modalities in EHRs, clinical notes…
The Pupil Has Become the Master: Teacher-Student Model-Based Word Embedding Distillation with Ensemble Learning
Bonggun Shin, Hao Yang, Jinho D. Choi
Recent advances in deep learning have facilitated the demand of neural models for real applications. In practice, these applications often need to be deployed with limited resource…
Classification of Radiology Reports Using Neural Attention Models
Bonggun Shin, Falgun H. Chokshi, Timothy Lee +1
The electronic health record (EHR) contains a large amount of multi-dimensional and unstructured clinical data of significant operational and research value. Distinguished from pre…
Lexicon Integrated CNN Models with Attention for Sentiment Analysis
Bonggun Shin, Timothy Lee, Jinho D. Choi
With the advent of word embeddings, lexicons are no longer fully utilized for sentiment analysis although they still provide important features in the traditional setting. This pap…