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2020 – today
- 2023
[c9]Ruoxi Sun, Chun-Liang Li, Sercan Ö. Arik, Michael W. Dusenberry, Chen-Yu Lee, Tomas Pfister:
Neural Spline Search for Quantile Probabilistic Modeling. AAAI 2023: 9927-9934- [c8]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 - [i13]Ruoxi Sun, Chun-Liang Li, Sercan Ö. Arik, Michael W. Dusenberry, Chen-Yu Lee, Tomas Pfister:
Neural Spline Search for Quantile Probabilistic Modeling. CoRR abs/2301.04857 (2023) - [i12]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) - [i11]Benoit Dherin, Huiyi Hu, Jie Ren, Michael W. Dusenberry, Balaji Lakshminarayanan:
Morse Neural Networks for Uncertainty Quantification. CoRR abs/2307.00667 (2023) - 2022
- [i10]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) - [i9]Neil Band, Tim G. J. Rudner, Qixuan Feng, Angelos Filos, Zachary Nado, Michael W. Dusenberry, Ghassen Jerfel, Dustin Tran, Yarin Gal:
Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks. CoRR abs/2211.12717 (2022) - 2021
- [j2]Sercan Ö. Arik
, Joel Shor, Rajarishi Sinha, Jinsung Yoon, Joseph R. Ledsam, Long T. Le, Michael W. Dusenberry, Nathanael C. Yoder, Kris Popendorf, Arkady Epshteyn, Johan Euphrosine, Elli Kanal, Isaac Jones, Chun-Liang Li, Beth Luan, Joe Mckenna, Vikas Menon, Shashank Singh, Mimi Sun, Ashwin Sura Ravi, Leyou Zhang, Dario Sava, Kane Cunningham, Hiroki Kayama, Thomas C. Tsai, Daisuke Yoneoka, Shuhei Nomura, Hiroaki Miyata, Tomas Pfister:
A prospective evaluation of AI-augmented epidemiology to forecast COVID-19 in the USA and Japan. npj Digit. Medicine 4 (2021) - [c7]Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W. Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, Dustin Tran:
Combining Ensembles and Data Augmentation Can Harm Your Calibration. ICLR 2021 - [c6]Neil Band, Tim G. J. Rudner, Qixuan Feng, Angelos Filos, Zachary Nado, Mike Dusenberry, Ghassen Jerfel, Dustin Tran, Yarin Gal:
Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks. NeurIPS Datasets and Benchmarks 2021 - [i8]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) - 2020
- [c5]Edward Choi, Zhen Xu, Yujia Li, Michael Dusenberry, Gerardo Flores, Emily Xue, Andrew M. Dai:
Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer. AAAI 2020: 606-613 - [c4]Michael W. Dusenberry, Dustin Tran, Edward Choi, Jonas Kemp, Jeremy Nixon, Ghassen Jerfel, Katherine A. Heller, Andrew M. Dai:
Analyzing the role of model uncertainty for electronic health records. CHIL 2020: 204-213 - [c3]Michael Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-An Ma, Jasper Snoek, Katherine A. Heller, Balaji Lakshminarayanan, Dustin Tran:
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors. ICML 2020: 2782-2792 - [i7]Michael W. Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-An Ma, Jasper Snoek, Katherine A. Heller, Balaji Lakshminarayanan, Dustin Tran:
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors. CoRR abs/2005.07186 (2020) - [i6]Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W. Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, Dustin Tran:
Combining Ensembles and Data Augmentation can Harm your Calibration. CoRR abs/2010.09875 (2020)
2010 – 2019
- 2019
- [c2]Jeremy Nixon, Michael W. Dusenberry, Linchuan Zhang, Ghassen Jerfel, Dustin Tran:
Measuring Calibration in Deep Learning. CVPR Workshops 2019: 38-41 - [c1]Dustin Tran, Mike Dusenberry, Mark van der Wilk, Danijar Hafner:
Bayesian Layers: A Module for Neural Network Uncertainty. NeurIPS 2019: 14633-14645 - [i5]Jeremy Nixon, Mike Dusenberry, Linchuan Zhang, Ghassen Jerfel, Dustin Tran:
Measuring Calibration in Deep Learning. CoRR abs/1904.01685 (2019) - [i4]Michael W. Dusenberry, Dustin Tran, Edward Choi, Jonas Kemp, Jeremy Nixon, Ghassen Jerfel, Katherine A. Heller, Andrew M. Dai:
Analyzing the Role of Model Uncertainty for Electronic Health Records. CoRR abs/1906.03842 (2019) - [i3]Edward Choi, Zhen Xu, Yujia Li, Michael W. Dusenberry, Gerardo Flores, Yuan Xue, Andrew M. Dai:
Graph Convolutional Transformer: Learning the Graphical Structure of Electronic Health Records. CoRR abs/1906.04716 (2019) - 2018
- [i2]Niketan Pansare, Michael Dusenberry, Nakul Jindal, Matthias Boehm, Berthold Reinwald, Prithviraj Sen:
Deep Learning with Apache SystemML. CoRR abs/1802.04647 (2018) - [i1]Dustin Tran, Michael W. Dusenberry, Mark van der Wilk, Danijar Hafner:
Bayesian Layers: A Module for Neural Network Uncertainty. CoRR abs/1812.03973 (2018) - 2016
- [j1]Matthias Boehm, Michael Dusenberry, Deron Eriksson, Alexandre V. Evfimievski, Faraz Makari Manshadi, Niketan Pansare, Berthold Reinwald, Frederick Reiss, Prithviraj Sen, Arvind Surve, Shirish Tatikonda:
SystemML: Declarative Machine Learning on Spark. Proc. VLDB Endow. 9(13): 1425-1436 (2016)
Coauthor Index
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