4 citations · 11 across the 10 of their papers we have counts for
7 papers · 1 filter
Universal EHR Federated Learning Framework
Junu Kim, Kyunghoon Hur, Seongjun Yang +1
Federated learning (FL) is the most practical multi-source learning method for electronic healthcare records (EHR). Despite its guarantee of privacy protection, the wide applicatio…
Specializing Multi-domain NMT via Penalizing Low Mutual Information
Jiyoung Lee, Hantae Kim, Hyunchang Cho +2
Multi-domain Neural Machine Translation (NMT) trains a single model with multiple domains. It is appealing because of its efficacy in handling multiple domains within one model. An…
Do Language Models Understand Measurements?
Sungjin Park, Seungwoo Ryu, Edward Choi
Recent success of pre-trained language models (PLMs) has stimulated interest in their ability to understand and work with numbers. Yet, the numerical reasoning over measurements ha…
Correlation between Alignment-Uniformity and Performance of Dense Contrastive Representations
Jong Hak Moon, Wonjae Kim, Edward Choi
Recently, dense contrastive learning has shown superior performance on dense prediction tasks compared to instance-level contrastive learning. Despite its supremacy, the properties…
Uncertainty-Aware Text-to-Program for Question Answering on Structured Electronic Health Records
Daeyoung Kim, Seongsu Bae, Seungho Kim +1
Question Answering on Electronic Health Records (EHR-QA) has a significant impact on the healthcare domain, and it is being actively studied. Previous research on structured EHR-QA…
Graph-Text Multi-Modal Pre-training for Medical Representation Learning
Sungjin Park, Seongsu Bae, Jiho Kim +2
As the volume of Electronic Health Records (EHR) sharply grows, there has been emerging interest in learning the representation of EHR for healthcare applications. Representation l…