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Isotropic Gaussian priors are the de facto standard for modern Bayesian neural network inference.
The probable error of a mean
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Evidence optimization techniques for estimating stimulus-response functions
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Anuj Srivastava, Ann B Lee, Eero P Simoncelli, and S-C Zhu · 2003
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Rational Construction of Stochastic Numerical Methods for Molecular Sampling
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Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Weight uncertainty in neural network
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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Probabilistic backpropagation for scalable learning of Bayesian neural networks
José Miguel Hernández-Lobato and Ryan Adams · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Variational dropout and the local reparameterization trick
Diederik P Kingma, Tim Salimans, and Max Welling · 2015
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Obtaining well calibrated probabilities using Bayesian binning
Mahdi Pakdaman Naeini, Gregory F Cooper, and Milos Hauskrecht · 2015
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A scale mixture perspective of multiplicative noise in neural networks
Eric T Nalisnick, Anima Anandkumar, and Padhraic Smyth · 2015
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Improving the computational efficiency of fully Bayes inference and assessing the effect of misspecification of hyperparameters in whole-genome prediction models
Wenzhao Yang, Chunyu Chen, and Robert J Tempelman · 2015
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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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Can you trust your model’s uncertainty? Evaluating predictive uncertainty under dataset shift
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 2019
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Interpretable outcome prediction with sparse Bayesian neural networks in intensive care
Hiske Overweg, Anna-Lena Popkes, Ari Ercole, Yingzhen Li, José Miguel Hernández-Lobato, Yordan Zaykov, and Cheng Zhang · 2019
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PyTorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Functional variational Bayesian neural networks
Shengyang Sun, Guodong Zhang, Jiaxin Shi, and Roger Grosse · 2019
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Jug: Software for parallel reproducible computation in Python
Luis Pedro Coelho · 2017
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Model selection in Bayesian neural networks via horseshoe priors
Soumya Ghosh and Finale Doshi-Velez · 2017
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The Sacred Infrastructure for Computational Research
Klaus Greff, Aaron Klein, Martin Chovanec, Frank Hutter, and Jürgen Schmidhuber · 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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Deep neural networks as Gaussian processes
Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel S Schoenholz, Jeffrey Pennington, and Jascha Sohl-Dickstein · 2017
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Russell Tsuchida, Fred Roosta, and Marcus Gallagher · 2019
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Wide feedforward or recurrent neural networks of any architecture are Gaussian processes
Greg Yang · 2019
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Cyclical stochastic gradient MCMC for Bayesian deep learning
Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, and Andrew Gordon Wilson · 2019
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Laurence Aitchison, Adam X Yang, and Sebastian W Ober · 2020
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Informative Gaussian scale mixture priors for Bayesian neural networks
Tianyu Cui, A. Havulinna, P. Marttinen, and S. Kaski · 2020
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Efficient and scalable Bayesian neural nets with rank-1 factors
Michael W Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-an Ma, Jasper Snoek, Katherine Heller, Balaji Lakshminarayanan, and Dustin Tran · 2020
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The heavy-tail phenomenon in SGD
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Noise contrastive priors for functional uncertainty
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Subspace inference for Bayesian deep learning
Pavel Izmailov, Wesley J Maddox, Polina Kirichenko, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson · 2020
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Hierarchical Gaussian process priors for Bayesian neural network weights
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Takuo Matsubara, Chris J Oates, and François-Xavier Briol · 2020
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Sebastian W Ober and Laurence Aitchison · 2020
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Expressive priors in Bayesian neural networks: Kernel combinations and periodic functions
Tim Pearce, Russell Tsuchida, Mohamed Zaki, Alexandra Brintrup, and Andy Neely · 2020
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Stable behaviour of infinitely wide deep neural networks
Stefano Peluchetti, Stefano Favaro, and Sandra Fortini · 2020
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PAC-Bayes analysis beyond the usual bounds
Omar Rivasplata, Ilja Kuzborskij, Csaba Szepesvári, and John Shawe-Taylor · 2020
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PACOH: Bayes-optimal meta-learning with PAC-guarantees
Jonas Rothfuss, Vincent Fortuin, and Andreas Krause · 2020
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Jakub Swiatkowski, Kevin Roth, Bastiaan S Veeling, Linh Tran, Joshua V Dillon, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, and Sebastian Nowozin · 2020
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All you need is a good functional prior for Bayesian deep learning
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On the role of data in PAC-Bayes
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Exact Langevin dynamics with stochastic gradients
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Correlated weights in infinite limits of deep convolutional neural networks
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The promises and pitfalls of deep kernel learning
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