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Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions


Apr 08, 2021
Abhijit Guha Roy, Jie Ren, Shekoofeh Azizi, Aaron Loh, Vivek Natarajan, Basil Mustafa, Nick Pawlowski, Jan Freyberg, Yuan Liu, Zach Beaver, Nam Vo, Peggy Bui, Samantha Winter, Patricia MacWilliams, Greg S. Corrado, Umesh Telang, Yun Liu, Taylan Cemgil, Alan Karthikesalingam, Balaji Lakshminarayanan, Jim Winkens

* Under Review, 19 Pages 

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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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Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift


Jul 17, 2020
Zachary Nado, Shreyas Padhy, D. Sculley, Alexander D'Amour, Balaji Lakshminarayanan, Jasper Snoek


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Bayesian Deep Ensembles via the Neural Tangent Kernel


Jul 11, 2020
Bobby He, Balaji Lakshminarayanan, Yee Whye Teh


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Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks


Jul 10, 2020
Shreyas Padhy, Zachary Nado, Jie Ren, Jeremiah Liu, Jasper Snoek, Balaji Lakshminarayanan


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Density of States Estimation for Out-of-Distribution Detection


Jun 22, 2020
Warren R. Morningstar, Cusuh Ham, Andrew G. Gallagher, Balaji Lakshminarayanan, Alexander A. Alemi, Joshua V. Dillon

* Submitted to NeurIPS. Corrected footnote from: "34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada" to "Preprint. Under review." 

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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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AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty


Dec 05, 2019
Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan

* Code available at https://github.com/google-research/augmix 

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Normalizing Flows for Probabilistic Modeling and Inference


Dec 05, 2019
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan

* Review article. 60 pages, 4 figures 

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Deep Ensembles: A Loss Landscape Perspective


Dec 05, 2019
Stanislav Fort, Huiyi Hu, Balaji Lakshminarayanan


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Detecting Out-of-Distribution Inputs to Deep Generative Models Using a Test for Typicality


Jun 07, 2019
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Balaji Lakshminarayanan


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Likelihood Ratios for Out-of-Distribution Detection


Jun 07, 2019
Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan


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Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift


Jun 06, 2019
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, D Sculley, Sebastian Nowozin, Joshua V. Dillon, Balaji Lakshminarayanan, Jasper Snoek


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Hybrid Models with Deep and Invertible Features


Feb 07, 2019
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, Balaji Lakshminarayanan


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Adapting Auxiliary Losses Using Gradient Similarity


Dec 05, 2018
Yunshu Du, Wojciech M. Czarnecki, Siddhant M. Jayakumar, Razvan Pascanu, Balaji Lakshminarayanan


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Do Deep Generative Models Know What They Don't Know?


Oct 22, 2018
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, Balaji Lakshminarayanan


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Learning from Delayed Outcomes with Intermediate Observations


Jul 24, 2018
Timothy A. Mann, Sven Gowal, Ray Jiang, Huiyi Hu, Balaji Lakshminarayanan, Andras Gyorgy


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Distribution Matching in Variational Inference


Jun 12, 2018
Mihaela Rosca, Balaji Lakshminarayanan, Shakir Mohamed


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Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step


Feb 20, 2018
William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M. Dai, Shakir Mohamed, Ian Goodfellow

* 18 pages 

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Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles


Nov 04, 2017
Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell

* NIPS 2017 

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Variational Approaches for Auto-Encoding Generative Adversarial Networks


Oct 21, 2017
Mihaela Rosca, Balaji Lakshminarayanan, David Warde-Farley, Shakir Mohamed


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Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server


Sep 07, 2017
Leonard Hasenclever, Stefan Webb, Thibaut Lienart, Sebastian Vollmer, Balaji Lakshminarayanan, Charles Blundell, Yee Whye Teh

* Journal of Machine Learning Research 18 (2017) 1-37 
* 37 pages, 7 figures 

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The Cramer Distance as a Solution to Biased Wasserstein Gradients


May 30, 2017
Marc G. Bellemare, Ivo Danihelka, Will Dabney, Shakir Mohamed, Balaji Lakshminarayanan, Stephan Hoyer, Rémi Munos


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Comparison of Maximum Likelihood and GAN-based training of Real NVPs


May 15, 2017
Ivo Danihelka, Balaji Lakshminarayanan, Benigno Uria, Daan Wierstra, Peter Dayan


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Learning Deep Nearest Neighbor Representations Using Differentiable Boundary Trees


Feb 28, 2017
Daniel Zoran, Balaji Lakshminarayanan, Charles Blundell


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Learning in Implicit Generative Models


Feb 27, 2017
Shakir Mohamed, Balaji Lakshminarayanan


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