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Accurate estimation of aleatoric and epistemic uncertainty is crucial to build safe and reliable systems.
Optimal information processing and bayes’s theorem
Arnold Zellner · 1988
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A pac analysis of a bayesian estimator
John Shawe-Taylor and Robert C. Williamson · 1997
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Learning multiple layers of features from tiny images
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MNIST handwritten digit database
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Reading digits in natural images with unsupervised feature learning
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Weight uncertainty in neural networks
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Deep residual learning for image recognition
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Variational inference with normalizing flows
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Very deep convolutional networks for large-scale image recognition
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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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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
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Improved variational inference with inverse autoregressive flow
Durk P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
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Conditional image generation with pixelcnn decoders
Aaron van den Oord, Nal Kalchbrenner, Lasse Espeholt, koray kavukcuoglu, Oriol Vinyals, and Alex Graves · 2016
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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The power of certainty: A dirichlet-multinomial model for belief propagation
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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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Simple and scalable predictive uncertainty estimation using deep ensembles
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Masked autoregressive flow for density estimation
George Papamakarios, Theo Pavlakou, and Iain Murray · 2017
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Generative ensembles for robust anomaly detection, 2019
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Scalable reversible generative models with free-form continuous dynamics
Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, and David Duvenaud · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
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A simple baseline for bayesian uncertainty in deep learning
Wesley J Maddox, Pavel Izmailov, Timur Garipov, Dmitry P Vetrov, and Andrew Gordon Wilson · 2019
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Reverse kl-divergence training of prior networks: Improved uncertainty and adversarial robustness
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Do deep generative models know what they don’t know?
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Practical deep learning with bayesian principles
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Pytorch: An imperative style, high-performance deep learning library
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Can you trust your modelś uncertainty? evaluating predictive uncertainty under dataset shift
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Your classifier is secretly an energy based model and you should treat it like one
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Why Normalizing Flows Fail to Detect Out-of-Distribution Data
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