no code implementations • 2 Nov 2022 • Weiyao Wang, Byung-Hak Kim, Varun Ganapathi
Recent advances in self-supervised learning (SSL) using large models to learn visual representations from natural images are rapidly closing the gap between the results produced by fully supervised learning and those produced by SSL on downstream vision tasks.
no code implementations • 28 Oct 2022 • Byung-Hak Kim, Zhongfen Deng, Philip S. Yu, Varun Ganapathi
The medical codes prediction problem from clinical notes has received substantial interest in the NLP community, and several recent studies have shown the state-of-the-art (SOTA) code prediction results of full-fledged deep learning-based methods.
no code implementations • 10 Jul 2021 • Byung-Hak Kim, Varun Ganapathi
Prediction of medical codes from clinical notes is both a practical and essential need for every healthcare delivery organization within current medical systems.
Ranked #4 on Medical Code Prediction on MIMIC-III
Medical Code Prediction Multi-Label Classification Of Biomedical Texts +1
no code implementations • 13 Jul 2020 • Byung-Hak Kim, Seshadri Sridharan, Andy Atwal, Varun Ganapathi
Each year, almost 10% of claims are denied by payers (i. e., health insurance plans).
no code implementations • 4 Jul 2019 • Byung-Hak Kim, Varun Ganapathi
We present Lumi\`ereNet, a simple, modular, and completely deep-learning based architecture that synthesizes, high quality, full-pose headshot lecture videos from instructor's new audio narration of any length.
no code implementations • 7 Sep 2018 • Byung-Hak Kim, Ethan Vizitei, Varun Ganapathi
Increasingly fast development and update cycle of online course contents, and diverse demographics of students in each online classroom, make student performance prediction in real-time (before the course finishes) and/or on curriculum without specific historical performance data available interesting topics for both industrial research and practical needs.
no code implementations • 19 Apr 2018 • Byung-Hak Kim, Ethan Vizitei, Varun Ganapathi
Student performance prediction - where a machine forecasts the future performance of students as they interact with online coursework - is a challenging problem.