Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Ganapathi, Varun"'
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 vis
Externí odkaz:
http://arxiv.org/abs/2211.01165
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. Ho
Externí odkaz:
http://arxiv.org/abs/2210.15882
Autor:
Ganapathi, Varun
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 effects of scattered light and pointing jitter. The background subtraction
Externí odkaz:
http://arxiv.org/abs/2209.09848
Autor:
Kim, Byung-Hak, Ganapathi, Varun
Prediction of medical codes from clinical notes is both a practical and essential need for every healthcare delivery organization within current medical systems. Automating annotation will save significant time and excessive effort spent by human cod
Externí odkaz:
http://arxiv.org/abs/2107.10650
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 of payment) from claims data with a high degree of accuracy and preci
Externí odkaz:
http://arxiv.org/abs/2007.06229
Autor:
Kim, Byung-Hak, Ganapathi, Varun
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. Unlike prior works, Lumi\`ereNet is ent
Externí odkaz:
http://arxiv.org/abs/1907.02253
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 specif
Externí odkaz:
http://arxiv.org/abs/1809.06686
Student performance prediction - where a machine forecasts the future performance of students as they interact with online coursework - is a challenging problem. Reliable early-stage predictions of a student's future performance could be critical to
Externí odkaz:
http://arxiv.org/abs/1804.07405
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 exact inference with approximate methods such as loopy belief propagation
Externí odkaz:
http://arxiv.org/abs/1206.3257
Publikováno v:
Computer Vision - ECCV 2012 (9783642337826); 2012, p738-751, 14p
