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 | 1670 | 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 | 701 | 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 | 511 | 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 (1), 64-77, 2020 | 372 | 2020 |
Exploring the limits of out-of-distribution detection S Fort, J Ren, B Lakshminarayanan Advances in Neural Information Processing Systems 34, 7068-7081, 2021 | 290 | 2021 |
Alignment-free oligonucleotide frequency dissimilarity measure improves prediction of hosts from metagenomically-derived viral sequences NA Ahlgren, J Ren, YY Lu, JA Fuhrman, F Sun Nucleic acids research 45 (1), 39-53, 2017 | 262 | 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 | 190 | 2014 |
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 | 156 | 2021 |
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 | 103 | 2022 |
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 | 98 | 2021 |
Comparison of metagenomic samples using sequence signatures B Jiang, K Song, J Ren, M Deng, F Sun, X Zhang BMC genomics 13, 1-17, 2012 | 93 | 2012 |
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 | 89 | 2020 |
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 | 88 | 2018 |
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 | 87 | 2022 |
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 | 87 | 2018 |
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 | 85 | 2013 |
CAFE: aCcelerated Alignment-FrEe sequence analysis YY Lu, K Tang, J Ren, JA Fuhrman, MS Waterman, F Sun Nucleic acids research 45 (W1), W554-W559, 2017 | 70 | 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 | 65 | 2019 |
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 | 50 | 2017 |
Revisiting one-vs-all classifiers for predictive uncertainty and out-of-distribution detection in neural networks S Padhy, Z Nado, J Ren, J Liu, J Snoek, B Lakshminarayanan arXiv preprint arXiv:2007.05134, 2020 | 43 | 2020 |