J
Jie Ren
Researcher at University of Nottingham
Publications - 105
Citations - 4690
Jie Ren is an academic researcher from University of Nottingham. The author has contributed to research in topics: Boundary layer & Metagenomics. The author has an hindex of 22, co-authored 85 publications receiving 2659 citations. Previous affiliations of Jie Ren include Delft University of Technology & Tsinghua University.
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Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
Yaniv Ovadia,Emily Fertig,Jie Ren,Zachary Nado,D. Sculley,Sebastian Nowozin,Joshua V. Dillon,Balaji Lakshminarayanan,Jasper Snoek +8 more
TL;DR: A large-scale benchmark of existing state-of-the-art methods on classification problems and the effect of dataset shift on accuracy and calibration is presented, finding that traditional post-hoc calibration does indeed fall short, as do several other previous methods.
Proceedings Article
Likelihood Ratios for Out-of-Distribution Detection
Jie Ren,Peter J. Liu,Emily Fertig,Jasper Snoek,Ryan Poplin,Mark A. DePristo,Joshua V. Dillon,Balaji Lakshminarayanan +7 more
TL;DR: This paper proposed a likelihood ratio method for deep generative models which effectively corrects for these confounding background statistics and achieved state-of-the-art performance on the genomics dataset.
Proceedings Article
Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift
Yaniv Ovadia,Emily Fertig,Jie Ren,Zachary Nado,D. Sculley,Sebastian Nowozin,Joshua V. Dillon,Balaji Lakshminarayanan,Jasper Snoek +8 more
TL;DR: In this paper, the authors present a large-scale benchmark of existing state-of-the-art methods on classification problems and investigate the effect of dataset shift on accuracy and calibration.
Journal ArticleDOI
VirFinder: a novel k -mer based tool for identifying viral sequences from assembled metagenomic data
Jie Ren,Nathan A. Ahlgren,Nathan A. Ahlgren,Yang Young Lu,Jed A. Fuhrman,Fengzhu Sun,Fengzhu Sun +6 more
TL;DR: VirFinder is the first k-mer frequency based, machine learning method for virus contig identification that entirely avoids gene-based similarity searches and will significantly improve prokaryotic viral sequence identification, especially for metagenomic-based studies of viral ecology.
Journal ArticleDOI
Identifying viruses from metagenomic data using deep learning.
Jie Ren,Kai Song,Chao Deng,Nathan A. Ahlgren,Jed A. Fuhrman,Yi Li,Xiaohui Xie,Ryan Poplin,Fengzhu Sun +8 more
TL;DR: Powered by deep learning and high throughput sequencing metagenomic data, DeepVirFinder significantly improved the accuracy of viral identification and will assist the study of viruses in the era of metagenomics.