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Ryan Poplin
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2020 – today
- 2021
- [i4]Trung Le, Ryan Poplin, Fred Bertsch, Andeep Singh Toor, Margaret L. Oh:
SyntheticFur dataset for neural rendering. CoRR abs/2105.06409 (2021) - 2020
- [j2]Jie Ren, Kai Song, Chao Deng, Nathan A. Ahlgren
, Jed A. Fuhrman, Yi Li, Xiaohui Xie, Ryan Poplin, Fengzhu Sun:
Identifying viruses from metagenomic data using deep learning. Quant. Biol. 8(1): 64-77 (2020)
2010 – 2019
- 2019
- [j1]Cory Y. McLean
GenomeWarp: an alignment-based variant coordinate transformation. Bioinform. 35(21): 4389-4391 (2019) - [c3]Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan:
Likelihood Ratios for Out-of-Distribution Detection. NeurIPS 2019: 14680-14691 - [c2]Peyton Greenside, Justin M. Zook, Marc Salit, Madeleine L. Cule, Ryan Poplin, Mark A. DePristo:
CrowdVariant: a crowdsourcing approach to classify copy number variants. PSB 2019: 224-235 - [i3]Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan:
Likelihood Ratios for Out-of-Distribution Detection. CoRR abs/1906.02845 (2019) - 2017
- [c1]Yaniv Ovadia, Yoni Halpern, Dilip Krishnan, Josh Livni, Daniel E. Newburger, Ryan Poplin, Tiantian Zha, D. Sculley:
Learning to Count Mosquitoes for the Sterile Insect Technique. KDD 2017: 1943-1949 - [i2]Ryan Poplin, Avinash V. Varadarajan, Katy Blumer, Yun Liu, Michael V. McConnell, Gregory S. Corrado, Lily Peng, Dale R. Webster:
Predicting Cardiovascular Risk Factors from Retinal Fundus Photographs using Deep Learning. CoRR abs/1708.09843 (2017) - [i1]Avinash V. Varadarajan, Ryan Poplin, Katy Blumer, Christof Angermüller, Joe Ledsam, Reena Chopra, Pearse A. Keane, Greg Corrado, Lily Peng, Dale R. Webster:
Deep learning for predicting refractive error from retinal fundus images. CoRR abs/1712.07798 (2017)
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