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Revisiting the Calibration of Modern Neural Networks


Jun 15, 2021
Matthias Minderer, Josip Djolonga, Rob Romijnders, Frances Hubis, Xiaohua Zhai, Neil Houlsby, Dustin Tran, Mario Lucic


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Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning


Jun 07, 2021
Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W. Dusenberry, Sebastian Farquhar, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah 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


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RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems


Mar 14, 2021
Martin Mladenov, Chih-Wei Hsu, Vihan Jain, Eugene Ie, Christopher Colby, Nicolas Mayoraz, Hubert Pham, Dustin Tran, Ivan Vendrov, Craig Boutilier


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Combining Ensembles and Data Augmentation can Harm your Calibration


Oct 19, 2020
Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W. Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, Dustin Tran


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Training independent subnetworks for robust prediction


Oct 13, 2020
Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew M. Dai, Dustin Tran


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Hyperparameter Ensembles for Robustness and Uncertainty Quantification


Jun 24, 2020
Florian Wenzel, Jasper Snoek, Dustin Tran, Rodolphe Jenatton


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Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness


Jun 17, 2020
Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan


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Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors


May 14, 2020
Michael W. Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-an Ma, Jasper Snoek, Katherine Heller, Balaji Lakshminarayanan, Dustin Tran

* Code available at https://github.com/google/edward2 

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BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning


Feb 20, 2020
Yeming Wen, Dustin Tran, Jimmy Ba

* Eighth International Conference on Learning Representations (ICLR 2020) 

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On the Discrepancy between Density Estimation and Sequence Generation


Feb 17, 2020
Jason Lee, Dustin Tran, Orhan Firat, Kyunghyun Cho


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BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning


Feb 17, 2020
Yeming Wen, Dustin Tran, Jimmy Ba

* In International Conference on Learning Representations, 2020 

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Analyzing the Role of Model Uncertainty for Electronic Health Records


Jun 10, 2019
Michael W. Dusenberry, Dustin Tran, Edward Choi, Jonas Kemp, Jeremy Nixon, Ghassen Jerfel, Katherine Heller, Andrew M. Dai

* Presented at the ICML 2019 Workshop on Uncertainty & Robustness in Deep Learning. Code to be open-sourced 

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Discrete Flows: Invertible Generative Models of Discrete Data


May 24, 2019
Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole


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Measuring Calibration in Deep Learning


Apr 02, 2019
Jeremy Nixon, Mike Dusenberry, Linchuan Zhang, Ghassen Jerfel, Dustin Tran


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NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport


Mar 09, 2019
Matthew Hoffman, Pavel Sountsov, Joshua V. Dillon, Ian Langmore, Dustin Tran, Srinivas Vasudevan


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Bayesian Layers: A Module for Neural Network Uncertainty


Dec 11, 2018
Dustin Tran, Michael W. Dusenberry, Mark van der Wilk, Danijar Hafner

* Presented in NeurIPS 2018 workshop Bayesian Deep Learning. Code available at https://github.com/tensorflow/tensor2tensor 

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Simple, Distributed, and Accelerated Probabilistic Programming


Nov 29, 2018
Dustin Tran, Matthew Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul, Matthew Johnson, Rif A. Saurous

* Appears in Neural Information Processing Systems, 2018. Code available at http://bit.ly/2JpFipt 

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Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language


Nov 29, 2018
Matthew D. Hoffman, Matthew J. Johnson, Dustin Tran

* Appears in Neural Information Processing Systems, 2018. Code available at https://github.com/google-research/autoconj 

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Mesh-TensorFlow: Deep Learning for Supercomputers


Nov 05, 2018
Noam Shazeer, Youlong Cheng, Niki Parmar, Dustin Tran, Ashish Vaswani, Penporn Koanantakool, Peter Hawkins, HyoukJoong Lee, Mingsheng Hong, Cliff Young, Ryan Sepassi, Blake Hechtman


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Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors


Oct 31, 2018
Danijar Hafner, Dustin Tran, Timothy Lillicrap, Alex Irpan, James Davidson

* 9 pages, 5 figures 

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Image Transformer


Jun 15, 2018
Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Łukasz Kaiser, Noam Shazeer, Alexander Ku, Dustin Tran

* Appears in International Conference on Machine Learning, 2018. Code available at https://github.com/tensorflow/tensor2tensor 

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Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches


Apr 02, 2018
Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse

* Published as a conference paper at ICLR 2018 

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Operator Variational Inference


Mar 15, 2018
Rajesh Ranganath, Jaan Altosaar, Dustin Tran, David M. Blei

* Appears in Neural Information Processing Systems, 2016 

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Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data


Mar 10, 2018
Aki Vehtari, Andrew Gelman, Tuomas Sivula, Pasi Jylänki, Dustin Tran, Swupnil Sahai, Paul Blomstedt, John P. Cunningham, David Schiminovich, Christian Robert

* Updated. 31 pages (+ appendix) 

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TensorFlow Distributions


Nov 28, 2017
Joshua V. Dillon, Ian Langmore, Dustin Tran, Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matt Hoffman, Rif A. Saurous


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Variational Inference via $χ$-Upper Bound Minimization


Nov 12, 2017
Adji B. Dieng, Dustin Tran, Rajesh Ranganath, John Paisley, David M. Blei

* Neural Information Processing Systems, 2017 

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Hierarchical Implicit Models and Likelihood-Free Variational Inference


Nov 05, 2017
Dustin Tran, Rajesh Ranganath, David M. Blei

* Appears in Neural Information Processing Systems, 2017 

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Implicit Causal Models for Genome-wide Association Studies


Oct 30, 2017
Dustin Tran, David M. Blei


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Deep Probabilistic Programming


Mar 07, 2017
Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei

* Appears in International Conference on Learning Representations, 2017. A companion webpage for this paper is available at http://edwardlib.org/iclr2017 

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