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ORCIDJie Ren 0006
Person information
- affiliation: Google Brain, CA, USA
- affiliation (PhD 2017): University of Southern California, Department of Biological Sciences, Los Angeles, CA, USA
- affiliation: Peking University, Department of Probability and Statistics, Beijing, China
Other persons with the same name
- Jie Ren — disambiguation page
- Jie Ren 0001 — Google Inc., Santa Monica, CA, USA (and 1 more)
- Jie Ren 0002
![[0000-0001-5931-5881]](https://dblp1.uni-trier.de/img/orcid-mark.12x12.png "0000-0001-5931-5881")
— Nantong University, School of Information Science and Technology, Nantong, China - Jie Ren 0003 — University of Electronic Science and Technology of China, Chengdu, China
- Jie Ren 0004 — Beihang University, School of Electronic and Information Engineering, Beijing, China
- Jie Ren 0005 — National University of Singapore, Department of Physics, Singapore
- Jie Ren 0007 — Shaanxi Normal University, School of Computer Science, Xi'an, China (and 1 more)
- Jie Ren 0008 — University of Toronto, ON, Canada (and 1 more)
- Jie Ren 0009 — Fordham University, Gabelli School of Business, New York, NY, USA (and 1 more)
- Jie Ren 0010 — Beijing Jiaotong University, School of Electronics and Information Engineering, Beijing, China
- Jie Ren 0011 — China Yangtze Power Co., Yichang, China
- Jie Ren 0012 — Peking University, Institute of Computer Science and Technology, Beijing, China
- Jie Ren 0013 — Huawei Technologies, Shanghai, China (and 2 more)
- Jie Ren 0014 — Xi'an Polytechnic University, College of Electronics and Information, Xi'an, China (and 1 more)
- Jie Ren 0015 — College of William & Mary, Williamsburg, VA, USA (and 1 more)
- Jie Ren 0016 — Harbin Institute of Technology, School of Computer Science and Technology, Harbin, China
- Jie Ren 0017 — Tsinghua University, Beijing, China
- Jie Ren 0018 — Shanghai Jiao Tong University, China
- Jie Ren 0019 — Michigan State University, Department of Computer Science and Engineering, East Lansing, MI, USA
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Journal Articles
- 2023
[j13]Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zachary Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan:
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. J. Mach. Learn. Res. 24: 42:1-42:63 (2023)- 2022
- [j12]Shaokun An, Jie Ren, Fengzhu Sun, Lin Wan:
A New Context Tree Inference Algorithm for Variable Length Markov Chain Model with Applications to Biological Sequence Analyses. J. Comput. Biol. 29(8): 839-856 (2022) - [j11]Abhijit Guha Roy, Jie Ren, Shekoofeh Azizi, Aaron Loh, Vivek Natarajan, Basil Mustafa, Nick Pawlowski, Jan Freyberg, Yuan Liu, Zachary Beaver, Nam Vo, Peggy Bui, Samantha Winter, Patricia MacWilliams, Gregory S. Corrado, Umesh Telang, Yun Liu, A. Taylan Cemgil, Alan Karthikesalingam, Balaji Lakshminarayanan, Jim Winkens:
Does your dermatology classifier know what it doesn't know? Detecting the long-tail of unseen conditions. Medical Image Anal. 75: 102274 (2022) - 2021
- [j10]Xin Bai, Jie Ren, Yingying Fan, Fengzhu Sun:
KIMI: Knockoff Inference for Motif Identification from molecular sequences with controlled false discovery rate. Bioinform. 37(6): 759-766 (2021) - 2020
- [j9]Chao Deng, Timothy Daley, Peter Calabrese, Jie Ren, Andrew D. Smith:
Predicting the Number of Bases to Attain Sufficient Coverage in High-Throughput Sequencing Experiments. J. Comput. Biol. 27(7): 1130-1143 (2020) - [j8]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) - [j7]Lin Wan, Xin Kang, Jie Ren, Fengzhu Sun:
Confidence intervals for Markov chain transition probabilities based on next generation sequencing reads data. Quant. Biol. 8(2): 143-154 (2020) - 2017
- [j6]Mengge Zhang, Lianping Yang, Jie Ren, Nathan A. Ahlgren, Jed A. Fuhrman, Fengzhu Sun:
Prediction of virus-host infectious association by supervised learning methods. BMC Bioinform. 18(S-3): 143-154 (2017) - [j5]Yang Young Lu, Kujin Tang, Jie Ren, Jed A. Fuhrman, Michael S. Waterman, Fengzhu Sun:
CAFE: aCcelerated Alignment-FrEe sequence analysis. Nucleic Acids Res. 45(Webserver-Issue): W554-W559 (2017) - 2016
- [j4]Jie Ren, Kai Song, Minghua Deng, Gesine Reinert, Charles H. Cannon, Fengzhu Sun:
Inference of Markovian properties of molecular sequences from NGS data and applications to comparative genomics. Bioinform. 32(7): 993-1000 (2016) - 2014
- [j3]Kai Song, Jie Ren, Gesine Reinert, Minghua Deng, Michael S. Waterman, Fengzhu Sun:
New developments of alignment-free sequence comparison: measures, statistics and next-generation sequencing. Briefings Bioinform. 15(3): 343-353 (2014) - 2013
- [j2]Jie Ren, Kai Song, Fengzhu Sun, Minghua Deng, Gesine Reinert:
Multiple alignment-free sequence comparison. Bioinform. 29(21): 2690-2698 (2013) - [j1]Kai Song, Jie Ren, Zhiyuan Zhai, Xuemei Liu, Minghua Deng, Fengzhu Sun:
Alignment-Free Sequence Comparison Based on Next-Generation Sequencing Reads. J. Comput. Biol. 20(2): 64-79 (2013)
Conference and Workshop Papers
- 2023
- [c10]Yunhao Ge, Jie Ren, Andrew Gallagher, Yuxiao Wang, Ming-Hsuan Yang, Hartwig Adam, Laurent Itti, Balaji Lakshminarayanan, Jiaping Zhao:
Improving Zero-shot Generalization and Robustness of Multi-Modal Models. CVPR 2023: 11093-11101 - [c9]Kundan Krishna, Yao Zhao, Jie Ren, Balaji Lakshminarayanan, Jiaming Luo, Mohammad Saleh, Peter J. Liu:
Improving the Robustness of Summarization Models by Detecting and Removing Input Noise. EMNLP (Findings) 2023: 1324-1336 - [c8]Polina Zablotskaia, Du Phan, Joshua Maynez, Shashi Narayan, Jie Ren, Jeremiah Z. Liu:
On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Study. EMNLP (Findings) 2023: 2980-2992 - [c7]Jie Ren, Yao Zhao, Tu Vu, Peter J. Liu, Balaji Lakshminarayanan:
Self-Evaluation Improves Selective Generation in Large Language Models. ICBINB 2023: 49-64 - [c6]Jie Ren, Jiaming Luo, Yao Zhao, Kundan Krishna, Mohammad Saleh, Balaji Lakshminarayanan, Peter J. Liu:
Out-of-Distribution Detection and Selective Generation for Conditional Language Models. ICLR 2023 - [c5]James Urquhart Allingham, Jie Ren, Michael W. Dusenberry, Xiuye Gu, Yin Cui, Dustin Tran, Jeremiah Zhe Liu, Balaji Lakshminarayanan:
A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models. ICML 2023: 547-568 - 2021
- [c4]Stanislav Fort, Jie Ren, Balaji Lakshminarayanan:
Exploring the Limits of Out-of-Distribution Detection. NeurIPS 2021: 7068-7081 - 2019
- [c3]Jasper Snoek, Yaniv Ovadia, Emily Fertig, Balaji Lakshminarayanan, Sebastian Nowozin, D. Sculley, Joshua V. Dillon, Jie Ren, Zachary Nado:
Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift. NeurIPS 2019: 13969-13980 - [c2]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 - 2012
- [c1]Kai Song, Jie Ren, Zhiyuan Zhai, Xuemei Liu, Minghua Deng, Fengzhu Sun:
Alignment-Free Sequence Comparison Based on Next Generation Sequencing Reads: Extended Abstract. RECOMB 2012: 272-285
Informal and Other Publications
- 2023
- [i18]James Urquhart Allingham, Jie Ren, Michael W. Dusenberry, Jeremiah Zhe Liu, Xiuye Gu, Yin Cui, Dustin Tran, Balaji Lakshminarayanan:
A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models. CoRR abs/2302.06235 (2023) - [i17]Polina Zablotskaia, Du Phan, Joshua Maynez, Shashi Narayan, Jie Ren, Jeremiah Z. Liu:
On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Study. CoRR abs/2304.08653 (2023) - [i16]Yunhao Ge, Jie Ren, Jiaping Zhao, Kaifeng Chen, Andrew Gallagher, Laurent Itti, Balaji Lakshminarayanan:
Building One-class Detector for Anything: Open-vocabulary Zero-shot OOD Detection Using Text-image Models. CoRR abs/2305.17207 (2023) - [i15]Benoit Dherin, Huiyi Hu, Jie Ren, Michael W. Dusenberry, Balaji Lakshminarayanan:
Morse Neural Networks for Uncertainty Quantification. CoRR abs/2307.00667 (2023) - [i14]Jie Ren, Yao Zhao, Tu Vu, Peter J. Liu, Balaji Lakshminarayanan:
Self-Evaluation Improves Selective Generation in Large Language Models. CoRR abs/2312.09300 (2023) - 2022
- [i13]Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zack Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan:
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. CoRR abs/2205.00403 (2022) - [i12]Dustin Tran, Jeremiah Z. Liu, Michael W. Dusenberry, Du Phan, Mark Collier, Jie Ren, Kehang Han, Zi Wang, Zelda Mariet, Huiyi Hu, Neil Band, Tim G. J. Rudner, Karan Singhal, Zachary Nado, Joost van Amersfoort, Andreas Kirsch, Rodolphe Jenatton, Nithum Thain, Honglin Yuan, Kelly Buchanan, Kevin Murphy, D. Sculley, Yarin Gal, Zoubin Ghahramani, Jasper Snoek, Balaji Lakshminarayanan:
Plex: Towards Reliability using Pretrained Large Model Extensions. CoRR abs/2207.07411 (2022) - [i11]Jie Ren, Jiaming Luo, Yao Zhao, Kundan Krishna, Mohammad Saleh, Balaji Lakshminarayanan, Peter J. Liu:
Out-of-Distribution Detection and Selective Generation for Conditional Language Models. CoRR abs/2209.15558 (2022) - [i10]Yunhao Ge, Jie Ren, Yuxiao Wang, Andrew Gallagher, Ming-Hsuan Yang, Laurent Itti, Hartwig Adam, Balaji Lakshminarayanan, Jiaping Zhao:
Improving Zero-shot Generalization and Robustness of Multi-modal Models. CoRR abs/2212.01758 (2022) - [i9]Kundan Krishna, Yao Zhao, Jie Ren, Balaji Lakshminarayanan, Jiaming Luo, Mohammad Saleh, Peter J. Liu:
Improving the Robustness of Summarization Models by Detecting and Removing Input Noise. CoRR abs/2212.09928 (2022) - 2021
- [i8]Abhijit Guha Roy, Jie Ren, Shekoofeh Azizi, Aaron Loh, Vivek Natarajan, Basil Mustafa, Nick Pawlowski, Jan Freyberg, Yuan Liu, Zachary Beaver, Nam Vo, Peggy Bui, Samantha Winter, Patricia MacWilliams, Gregory S. Corrado, Umesh Telang, Yun Liu, A. Taylan Cemgil, Alan Karthikesalingam, Balaji Lakshminarayanan, Jim Winkens:
Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions. CoRR abs/2104.03829 (2021) - [i7]Stanislav Fort, Jie Ren, Balaji Lakshminarayanan:
Exploring the Limits of Out-of-Distribution Detection. CoRR abs/2106.03004 (2021) - [i6]Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W. Dusenberry, Sebastian Farquhar, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah Z. Liu, Zelda Mariet, Jeremy Nixon, Shreyas Padhy, Jie Ren, Tim G. J. Rudner, Yeming Wen, Florian Wenzel, Kevin Murphy, D. Sculley, Balaji Lakshminarayanan, Jasper Snoek, Yarin Gal, Dustin Tran:
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning. CoRR abs/2106.04015 (2021) - [i5]Jie Ren, Stanislav Fort, Jeremiah Z. Liu, Abhijit Guha Roy, Shreyas Padhy, Balaji Lakshminarayanan:
A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection. CoRR abs/2106.09022 (2021) - 2020
- [i4]Shreyas Padhy, Zachary Nado, Jie Ren, Jeremiah Z. Liu, Jasper Snoek, Balaji Lakshminarayanan:
Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks. CoRR abs/2007.05134 (2020) - 2019
- [i3]Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua V. Dillon, Balaji Lakshminarayanan, Jasper Snoek:
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift. CoRR abs/1906.02530 (2019) - [i2]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) - [i1]Peter J. Liu, Yu-An Chung, Jie Ren:
SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders. CoRR abs/1910.00998 (2019)
Coauthor Index
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