91 citations · 164 across the 15 of their papers we have counts for
15 papers
Multimodal Representation Learning of Cardiovascular Magnetic Resonance Imaging
Jielin Qiu, Peide Huang, Makiya Nakashima +10
Self-supervised learning is crucial for clinical imaging applications, given the lack of explicit labels in healthcare. However, conventional approaches that rely on precise vision…
RegCLR: A Self-Supervised Framework for Tabular Representation Learning in the Wild
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…
Medical Codes Prediction from Clinical Notes: From Human Coders to Machines
Byung-Hak Kim
Prediction of medical codes from clinical notes is a practical and essential need for every healthcare delivery organization within current medical systems. Automating annotation w…
Can Current Explainability Help Provide References in Clinical Notes to Support Humans Annotate Medical Codes?
Byung-Hak Kim, Zhongfen Deng, Philip S. Yu +1
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) c…
Read, Attend, and Code: Pushing the Limits of Medical Codes Prediction from Clinical Notes by Machines
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. Automating annotat…
Deep Claim: Payer Response Prediction from Claims Data with Deep Learning
Byung-Hak Kim, Seshadri Sridharan, Andy Atwal +1
Each year, almost 10% of claims are denied by payers (i.e., health insurance plans). With the cost to recover these denials and underpayments, predicting payer response (likelihood…
LumièreNet: Lecture Video Synthesis from Audio
Byung-Hak Kim, Varun Ganapathi
We present LumièreNet, a simple, modular, and completely deep-learning based architecture that synthesizes, high quality, full-pose headshot lecture videos from instructor's new au…
Deep Learning to Predict Student Outcomes
Byung-Hak Kim
The increasingly fast development cycle for online course contents, along with the diverse student demographics in each online classroom, make real-time student outcomes prediction…
Domain Adaptation for Real-Time Student Performance Prediction
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…
GritNet: Student Performance Prediction with Deep Learning
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. Reliable early-sta…
Joint Decoding of LDPC Codes and Finite-State Channels via Linear-Programming
Byung-Hak Kim, Henry D. Pfister
This paper considers the joint-decoding (JD) problem for finite-state channels (FSCs) and low-density parity-check (LDPC) codes. In the first part, the linear-programming (LP) deco…
An Iterative Joint Linear-Programming Decoding of LDPC Codes and Finite-State Channels
Byung-Hak Kim, Henry D. Pfister
In this paper, we introduce an efficient iterative solver for the joint linear-programming (LP) decoding of low-density parity-check (LDPC) codes and finite-state channels (FSCs).…
IMP: A Message-Passing Algorithmfor Matrix Completion
Byung-Hak Kim, Arvind Yedla, Henry D. Pfister
A new message-passing (MP) method is considered for the matrix completion problem associated with recommender systems. We attack the problem using a (generative) factor graph model…
On the Joint Decoding of LDPC Codes and Finite-State Channels via Linear Programming
Byung-Hak Kim, Henry D. Pfister
In this paper, the linear programming (LP) decoder for binary linear codes, introduced by Feldman, et al. is extended to joint-decoding of binary-input finite-state channels. In pa…
Message-Passing Inference on a Factor Graph for Collaborative Filtering
Byung-Hak Kim, Arvind Yedla, Henry D. Pfister
This paper introduces a novel message-passing (MP) framework for the collaborative filtering (CF) problem associated with recommender systems. We model the movie-rating prediction…