Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift Y Ovadia, E Fertig, J Ren, Z Nado, D Sculley, S Nowozin, J Dillon, ...
Advances in neural information processing systems 32, 2019
1166 2019 Likelihood ratios for out-of-distribution detection J Ren, PJ Liu, E Fertig, J Snoek, R Poplin, M Depristo, J Dillon, ...
Advances in neural information processing systems 32, 2019
514 2019 VirFinder: a novel k -mer based tool for identifying viral sequences from assembled metagenomic data J Ren, NA Ahlgren, YY Lu, JA Fuhrman, F Sun
Microbiome 5, 1-20, 2017
399 2017 Identifying viruses from metagenomic data using deep learning J Ren, K Song, C Deng, NA Ahlgren, JA Fuhrman, Y Li, X Xie, R Poplin, ...
Quantitative Biology 8, 64-77, 2020
255 2020 Alignment-free NA Ahlgren, J Ren, YY Lu, JA Fuhrman, F Sun
Nucleic acids research 45 (1), 39-53, 2017
219 2017 New developments of alignment-free sequence comparison: measures, statistics and next-generation sequencing K Song, J Ren, G Reinert, M Deng, MS Waterman, F Sun
Briefings in bioinformatics 15 (3), 343-353, 2014
177 2014 Exploring the limits of out-of-distribution detection S Fort, J Ren, B Lakshminarayanan
Advances in Neural Information Processing Systems 34, 7068-7081, 2021
141 2021 Comparison of metagenomic samples using sequence signatures B Jiang, K Song, J Ren, M Deng, F Sun, X Zhang
BMC genomics 13 (1), 1-17, 2012
90 2012 Alignment-free sequence comparison based on next-generation sequencing reads K Song, J Ren, Z Zhai, X Liu, M Deng, F Sun
Journal of computational biology 20 (2), 64-79, 2013
80 2013 Gut microbial and metabolomic profiles after fecal microbiota transplantation in pediatric ulcerative colitis patients DJ Nusbaum, F Sun, J Ren, Z Zhu, N Ramsy, N Pervolarakis, S Kunde, ...
FEMS microbiology ecology 94 (9), fiy133, 2018
72 2018 Alignment-free sequence analysis and applications J Ren, X Bai, YY Lu, K Tang, Y Wang, G Reinert, F Sun
Annual Review of Biomedical Data Science 1, 93-114, 2018
72 2018 A network-based integrated framework for predicting virus–prokaryote interactions W Wang, J Ren, K Tang, E Dart, JC Ignacio-Espinoza, JA Fuhrman, ...
NAR genomics and bioinformatics 2 (2), lqaa044, 2020
67 2020 Does your dermatology classifier know what it doesn’t know? detecting the long-tail of unseen conditions AG Roy, J Ren, S Azizi, A Loh, V Natarajan, B Mustafa, N Pawlowski, ...
Medical Image Analysis 75, 102274, 2022
64 2022 CAFE: aC celerated A lignment-F rE e sequence analysis YY Lu, K Tang, J Ren, JA Fuhrman, MS Waterman, F Sun
Nucleic acids research 45 (W1), W554-W559, 2017
60 2017 Uncertainty Baselines: Benchmarks for uncertainty & robustness in deep learning Z Nado, N Band, M Collier, J Djolonga, MW Dusenberry, S Farquhar, ...
arXiv preprint arXiv:2106.04015, 2021
59 2021 A simple fix to mahalanobis distance for improving near-ood detection J Ren, S Fort, J Liu, AG Roy, S Padhy, B Lakshminarayanan
arXiv preprint arXiv:2106.09022, 2021
58 2021 Prediction of virus-host infectious association by supervised learning methods M Zhang, L Yang, J Ren, NA Ahlgren, JA Fuhrman, F Sun
BMC bioinformatics 18, 143-154, 2017
45 2017 Can you trust your model’s uncertainty Y Ovadia, E Fertig, J Ren, Z Nado, D Sculley, S Nowozin, JV Dillon, ...
evaluating predictive uncertainty under dataset shift, 2019
44 2019 Plex: Towards reliability using pretrained large model extensions D Tran, J Liu, MW Dusenberry, D Phan, M Collier, J Ren, K Han, Z Wang, ...
arXiv preprint arXiv:2207.07411, 2022
34 2022 Inference of Markovian properties of molecular sequences from NGS data and applications to comparative genomics J Ren, K Song, M Deng, G Reinert, CH Cannon, F Sun
Bioinformatics 32 (7), 993-1000, 2016
32 2016 
Fengzhu SunUniversity of Southern CaliforniaVahvistettu sähköpostiosoite verkkotunnuksessa usc.edu
