91 citations · 145 across the 9 of their papers we have counts for
9 papers
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…
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…
Maximum Likelihood Systematic Effect Modeling and Matched Filtering to Detect Trans-Neptunian Objects with TESS
Varun Ganapathi
We present a pipeline for searching for trans-Neptunian objects (TNOs) using data from the TESS mission, that includes a novel optimization-based framework for subtracting the effe…
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…
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…
Constrained Approximate Maximum Entropy Learning of Markov Random Fields
Varun Ganapathi, David Vickrey, John Duchi +1
Parameter estimation in Markov random fields (MRFs) is a difficult task, in which inference over the network is run in the inner loop of a gradient descent procedure. Replacing exa…