15 citations · 32 across the 5 of their papers we have counts for
5 papers
Leveraging Biokinetic Knowledge Priors for Data-Scarce Bioprocess Modeling
Kyunghoon Hur, Eunjung Jeon, Hyun Woo Kim +2
While deep learning has accelerated drug discovery, its impact on biomanufacturing has been considerably more limited. The reason is data scarcity. Bioreactor experiments are high-…
Predictive Pipelined Decoding: A Compute-Latency Trade-off for Exact LLM Decoding
Seongjun Yang, Gibbeum Lee, Jaewoong Cho +2
This paper presents "Predictive Pipelined Decoding (PPD)," an approach that speeds up greedy decoding in Large Language Models (LLMs) while maintaining the exact same output as the…
EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records
Gyubok Lee, Hyeonji Hwang, Seongsu Bae +6
We present a new text-to-SQL dataset for electronic health records (EHRs). The utterances were collected from 222 hospital staff members, including physicians, nurses, and insuranc…
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…
Towards the Practical Utility of Federated Learning in the Medical Domain
Seongjun Yang, Hyeonji Hwang, Daeyoung Kim +4
Federated learning (FL) is an active area of research. One of the most suitable areas for adopting FL is the medical domain, where patient privacy must be respected. Previous resea…