V
Volodymyr Kuleshov
Researcher at Stanford University
Publications - 53
Citations - 3591
Volodymyr Kuleshov is an academic researcher from Stanford University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 19, co-authored 38 publications receiving 2487 citations. Previous affiliations of Volodymyr Kuleshov include McGill University & Illumina.
Papers
More filters
Journal ArticleDOI
A guide to deep learning in healthcare.
Andre Esteva,Alexandre Robicquet,Bharath Ramsundar,Volodymyr Kuleshov,Mark A. DePristo,Katherine Chou,Claire Cui,Greg S. Corrado,Sebastian Thrun,Jeffrey Dean +9 more
TL;DR: How these computational techniques can impact a few key areas of medicine and explore how to build end-to-end systems are described.
Posted Content
Algorithms for multi-armed bandit problems.
Volodymyr Kuleshov,Doina Precup +1 more
TL;DR: The findings demonstrate that bandit algorithms are attractive alternatives to current adaptive treatment allocation strategies and may guide the design of subsequent empirical evaluations.
Proceedings Article
Accurate Uncertainties for Deep Learning Using Calibrated Regression.
TL;DR: This paper proposed a simple procedure for calibrating any regression algorithm, which is inspired by Platt scaling and extends previous work on classification, when applied to Bayesian and probabilistic models, it can produce calibrated uncertainty estimates given enough data.
Journal ArticleDOI
Whole-genome haplotyping using long reads and statistical methods
Volodymyr Kuleshov,Volodymyr Kuleshov,Dan Xie,Rui Chen,Dmitry Pushkarev,Zhihai Ma,Tim Blauwkamp,Michael Kertesz,Michael Snyder +8 more
TL;DR: Using statistically aided, long-read haplotyping (SLRH), a rapid, accurate method that uses a statistical algorithm to take advantage of the partially phased information contained in long genomic fragments analyzed by short-read sequencing, this work phases 99% of single-nucleotide variants in three human genomes into long haplotype blocks 0.2–1 Mbp in length.
Posted Content
Accurate Uncertainties for Deep Learning Using Calibrated Regression
TL;DR: This article proposed a simple procedure for calibrating any regression algorithm, which is inspired by Platt scaling and extends previous work on classification, when applied to Bayesian and probabilistic models, it can produce calibrated uncertainty estimates given enough data.