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Uncertainty awareness is crucial to develop reliable machine learning models.
Methoden zur Berechnung der Gammafunktion für Komplexes Argument
Otto Rudolf Rocktäschel · 1922
Earlier work this paper cites.
A Course of Modern Analysis
Edmund Whittaker and George Watson · 1927
Earlier work this paper cites.
Conjugate priors for exponential families
Persi. Diaconis and Donald Ylvisaker · 1979
Earlier work this paper cites.
Fundamentals of Statistical Exponential Families: With Applications in Statistical Decision Theory
L. D. Brown · 1986
Earlier work this paper cites.
Optimal information processing and bayes’s theorem
Arnold Zellner · 1988
Earlier work this paper cites.
Prior Probabilities (1968)
Roger Rosenkrantz · 1989
Earlier work this paper cites.
A pac analysis of a bayesian estimator
John Shawe-Taylor and Robert C. Williamson · 1997
Earlier work this paper cites.
Unskilled and unaware of it: How difficulties in recognizing one’s own incompetence lead to inflated self-assessments
Justin Kruger and David Dunning · 2000
Earlier work this paper cites.
Bayesian Data Analysis
Andrew Gelman, John B. Carlin, Hal S. Stern, and Donald B. Rubin · 2004
Earlier work this paper cites.
Pattern Recognition and Machine Learning
Christopher M Bishop · 2006
Earlier work this paper cites.
Andrey Malinin, Sergey Chervontsev, Ivan Provilkov, and Mark Gales · 2006
Earlier work this paper cites.
Bayesian ensemble learning
Hugh Chipman, Edward George, and Robert Mcculloch · 2007
Earlier work this paper cites.
Strictly proper scoring rules, prediction, and estimation
Tilmann Gneiting and Adrian E Raftery · 2007
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Mnist handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
Earlier work this paper cites.
Entropies and cross-entropies of exponential families
Frank Nielsen and Richard Nock · 2010
Earlier work this paper cites.
Practical variational inference for neural networks
Alex Graves · 2011
Earlier work this paper cites.
Turning bayesian model averaging into bayesian model combination
K. Monteith, J. L. Carroll, K. Seppi, and T. Martinez · 2011
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
Earlier work this paper cites.
A note on the behavior of integrable functions at infinity
Constantin P. Niculescu and Florin Popovici · 2011
Earlier work this paper cites.
Bayesian classifier combination
Hyun-Chul Kim and Zoubin Ghahramani · 2012
Earlier work this paper cites.
Indoor segmentation and support inference from rgbd images
Pushmeet Kohli Nathan Silberman, Derek Hoiem and Rob Fergus · 2012
Earlier work this paper cites.
Dynamic bayesian combination of multiple imperfect classifiers
Edwin Simpson, Stephen Roberts, Ioannis Psorakis, and Arfon Smith · 2012
Earlier work this paper cites.
Existence of the limit at infinity for a function that is integrable on the half line
James Dix · 2013
Earlier work this paper cites.
Vision meets robotics: The kitti dataset
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun · 2013
Earlier work this paper cites.
Depth map prediction from a single image using a multi-scale deep network
David Eigen, Christian Puhrsch, and Rob Fergus · 2014
Earlier work this paper cites.
Event labeling combining ensemble detectors and background knowledge
Hadi Fanaee-T and João Gama · 2014
Earlier work this paper cites.
Weight uncertainty in neural networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
Earlier work this paper cites.
Made: Masked autoencoder for distribution estimation
Mathieu Germain, Karol Gregor, Iain Murray, and Hugo Larochelle · 2015
Earlier work this paper cites.
Probabilistic backpropagation for scalable learning of bayesian neural networks
Jose Miguel Hernandez-Lobato and Ryan Adams · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Earlier work this paper cites.
Deep exponential families
Rajesh Ranganath, Linpeng Tang, Laurent Charlin, and David Blei · 2015
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Variational inference with normalizing flows
Danilo Jimenez Rezende and Shakir Mohamed · 2015
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Yinda Zhang, Shuran Song, Ari Seff, and Jianxiong Xiao · 2015
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A general framework for updating belief distributions
P. G. Bissiri, C. C. Holmes, and S. G. Walker · 2016
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Uncertainty in Deep Learning
Yarin Gal · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Fine-tuning deep neural networks in continuous learning scenarios
Christoph Käding, Erik Rodner, Alexander Freytag, and Joachim Denzler · 2016
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Natural-parameter networks: A class of probabilistic neural networks
Feed-forward propagation in probabilistic neural networks with categorical and max layers
Alexander Shekhovtsov and Boris Flach · 2019
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Distribution calibration for regression
Hao Song, Tom Diethe, Meelis Kull, and Peter Flach · 2019
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Deep evidential regression
Alexander Amini, Wilko Schwarting, Ava Soleimany, and Daniela Rus · 2020
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On warm-starting neural network training
Jordan Ash and Ryan P Adams · 2020
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Posterior network: Uncertainty estimation without ood samples via density-based pseudo-counts
Bertrand Charpentier, Daniel Zügner, and Stephan Günnemann · 2020
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Efficient and scalable bayesian neural nets with rank-1 factors
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Hao Wang, Xingjian SHI, and Dit-Yan Yeung · 2016
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UCI machine learning repository
Dheeru Dua and Casey Graff · 2017
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Tiny imagenet
Andrej Karpathy Fei-Fei Li and Justin Johnson · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Masked autoregressive flow for density estimation
George Papamakarios, Theo Pavlakou, and Iain Murray · 2017
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Michael W. Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-An Ma, Jasper Snoek, Katherine Heller, Balaji Lakshminarayanan, and Dustin Tran · 2020
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Liberty or depth: Deep bayesian neural nets do not need complex weight posterior approximations
Sebastian Farquhar, Lewis Smith, and Yarin Gal · 2020
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On the expressiveness of approximate inference in bayesian neural networks
Andrew Y. K. Foong, David R. Burt, Yingzhen Li, and Richard E. Turner · 2020
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Your classifier is secretly an energy based model and you should treat it like one
Will Grathwohl, Kuan-Chieh Wang, Joern-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, and Kevin Swersky · 2020
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Why normalizing flows fail to detect out-of-distribution data
Polina Kirichenko, Pavel Izmailov, and Andrew Gordon Wilson · 2020
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Simple and principled uncertainty estimation with deterministic deep learning via distance awareness
Balaji Lakshminarayanan, Dustin Tran, Jeremiah Liu, Shreyas Padhy, Tania Bedrax-Weiss, and Zi Lin · 2020
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Perfect density models cannot guarantee anomaly detection
Charline Le Lan and Laurent Dinh · 2020
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Towards neural networks that provably know when they don’t know
Alexander Meinke and Matthias Hein · 2020
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Confidence-aware learning for deep neural networks
Jooyoung Moon, Jihyo Kim, Younghak Shin, and Sangheum Hwang · 2020
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Density of states estimation for out-of-distribution detection
Warren R. Morningstar, Cusuh Ham, Andrew G. Gallagher, Balaji Lakshminarayanan, Alexander A. Alemi, and Joshua V. Dillon · 2020
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Detecting out-of-distribution inputs to deep generative models using typicality
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, and Balaji Lakshminarayanan · 2020
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Towards maximizing the representation gap between in-domain & out-of-distribution examples
Jay Nandy, Wynne Hsu, and Mong-Li Lee · 2020
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Intra order-preserving functions for calibration of multi-class neural networks
Amir Rahimi, Amirreza Shaban, Ching-An Cheng, Richard Hartley, and Byron Boots · 2020
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Uncertainty-aware deep classifiers using generative models
Murat Sensoy, Lance Kaplan, Federico Cerutti, and Maryam Saleki · 2020
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Multifaceted uncertainty estimation for label-efficient deep learning
Weishi Shi, Xujiang Zhao, Feng Chen, and Qi Yu · 2020
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Uncertainty estimation using a single deep deterministic neural network
Joost van Amersfoort, Lewis Smith, Yee Whye Teh, and Yarin Gal · 2020
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Batchensemble: an alternative approach to efficient ensemble and lifelong learning
Yeming Wen, Dustin Tran, and Jimmy Ba · 2020
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Hyperparameter ensembles for robustness and uncertainty quantification
Florian Wenzel, Jasper Snoek, Dustin Tran, and Rodolphe Jenatton · 2020
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Contrastive training for improved out-of-distribution detection
Jim Winkens, Rudy Bunel, Abhijit Guha Roy, Robert Stanforth, Vivek Natarajan, Joseph R. Ledsam, Patricia MacWilliams, Pushmeet Kohli, Alan Karthikesalingam, Simon Kohl, Taylan Cemgil, S. M. Ali Eslami, and Olaf Ronneberger · 2020
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On out-of-distribution detection with energy-based models
Sven Elflein, Bertrand Charpentier, Daniel Zügner, and Stephan Günnemann · 2021
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A survey of uncertainty in deep neural networks
Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, Jongseok Lee, Matthias Humt, Jianxiang Feng, Anna Kruspe, Rudolph Triebel, Peter Jung, Ribana Roscher, Muhammad Shahzad, Wen Yang, Richard Bamler, and Xiao Xiang Zhu · 2021
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What are bayesian neural network posteriors really like?
Pavel Izmailov, Sharad Vikram, Matthew D Hoffman, and Andrew Gordon Wilson · 2021
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Evaluating robustness of predictive uncertainty estimation: Are dirichlet-based models reliable?
Anna-Kathrin Kopetzki, Bertrand Charpentier, Daniel Zügner, Sandhya Giri, and Stephan Günnemann · 2021
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Shifts: A dataset of real distributional shift across multiple large-scale tasks
Andrey Malinin, Neil Band, Ganshin, Alexander, German Chesnokov, Yarin Gal, Mark J. F. Gales, Alexey Noskov, Andrey Ploskonosov, Liudmila Prokhorenkova, Ivan Provilkov, Vatsal Raina, Vyas Raina, Roginskiy, Denis, Mariya Shmatova, Panos Tigas, and Boris Yangel · 2021
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Graph posterior network: Bayesian predictive uncertainty for node classification
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A survey on evidential deep learning for single-pass uncertainty estimation
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On feature collapse and deep kernel learning for single forward pass uncertainty
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