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Laplace approximation (LA) and its linearized variant (LLA) enable effortless adaptation of pretrained deep neural networks to Bayesian neural networks.
Functions ofpositive and negativetypeand theircommection with the theory ofintegral equations
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Über die praktische auflösung von integralgleichungen mit anwendungen auf randwertaufgaben
Evert J Nyström · 1930
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A practical Bayesian framework for backpropagation networks
David JC MacKay · 1992
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Bayesian methods for adaptive models
David John Cameron Mackay · 1992
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Keeping neural networks simple by minimizing the description length of the weights
Geoffrey Hinton and Drew Van Camp · 1993
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Bayesian Learning for Neural Networks
Radford M Neal · 1995
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Using the nyström method to speed up kernel machines
Christopher Williams and Matthias Seeger · 2000
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Variational inference in probabilistic models
Neil David Lawrence · 2001
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Gaussian processes for machine learning
Matthias Seeger · 2004
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On the nyström method for approximating a gram matrix for improved kernel-based learning
Petros Drineas, Michael W Mahoney, and Nello Cristianini · 2005
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Random features for large-scale kernel machines
Ali Rahimi and Benjamin Recht · 2007
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Weighted sums of random kitchen sinks: Replacing minimization with randomization in learning
Ali Rahimi and Benjamin Recht · 2008
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ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Variational learning of inducing variables in sparse gaussian processes
Michalis Titsias · 2009
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On the impact of kernel approximation on learning accuracy
Corinna Cortes, Mehryar Mohri, and Ameet Talwalkar · 2010
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Practical variational inference for neural networks
Alex Graves · 2011
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Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee W Teh · 2011
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Improved bounds for the nyström method with application to kernel classification
Rong Jin, Tianbao Yang, Mehrdad Mahdavi, Yu-Feng Li, and Zhi-Hua Zhou · 2013
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Stochastic gradient hamiltonian monte carlo
Tianqi Chen, Emily Fox, and Carlos Guestrin · 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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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Optimizing neural networks with kronecker-factored approximate curvature
James Martens and Roger Grosse · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Stein variational gradient descent: A general purpose Bayesian inference algorithm
Qiang Liu and Dilin Wang · 2016
Noisy natural gradient as variational inference
Guodong Zhang, Shengyang Sun, David Duvenaud, and Roger Grosse · 2018
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Rates of convergence for sparse variational gaussian process regression
David Burt, Carl Edward Rasmussen, and Mark Van Der Wilk · 2019
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’in-between’uncertainty in bayesian neural networks
Andrew YK Foong, Yingzhen Li, José Miguel Hernández-Lobato, and Richard E Turner · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Approximate inference turns deep networks into gaussian processes
Mohammad Emtiyaz Khan, Alexander Immer, Ehsan Abedi, and Maciej Korzepa · 2019
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Structured and efficient variational deep learning with matrix gaussian posteriors
Christos Louizos and Max Welling · 2016
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Orthogonal random features
Felix Xinnan X Yu, Ananda Theertha Suresh, Krzysztof M Choromanski, Daniel N Holtmann-Rice, and Sanjiv Kumar · 2016
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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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An empirical study on modeling and prediction of bitcoin prices with bayesian neural networks based on blockchain information
Huisu Jang and Jaewook Lee · 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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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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Practical deep learning with Bayesian principles
Kazuki Osawa, Siddharth Swaroop, Anirudh Jain, Runa Eschenhagen, Richard E Turner, Rio Yokota, and Mohammad Emtiyaz Khan · 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, et al · 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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Bayesadapter: Being bayesian, inexpensively and reliably, via bayesian fine-tuning
Zhijie Deng, Hao Zhang, Xiao Yang, Yinpeng Dong, and Jun Zhu · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Being bayesian, even just a bit, fixes overconfidence in relu networks
Agustinus Kristiadi, Matthias Hein, and Philipp Hennig · 2020
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Learning under model misspecification: Applications to variational and ensemble methods
Andres Masegosa · 2020
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Bayesian deep learning and a probabilistic perspective of generalization
Andrew Gordon Wilson and Pavel Izmailov · 2020
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Laplace redux-effortless bayesian deep learning
Erik Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, and Philipp Hennig · 2021
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Bayesian deep learning via subnetwork inference
Erik Daxberger, Eric Nalisnick, James U Allingham, Javier Antorán, and José Miguel Hernández-Lobato · 2021
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Mixtures of laplace approximations for improved post-hoc uncertainty in deep learning
Runa Eschenhagen, Erik Daxberger, Philipp Hennig, and Agustinus Kristiadi · 2021
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Major advancements in kernel function approximation
Deena P Francis and Kumudha Raimond · 2021
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Improving predictions of bayesian neural nets via local linearization
Alexander Immer, Maciej Korzepa, and Matthias Bauer · 2021
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Connections and equivalences between the nystr
Veit Wild, Motonobu Kanagawa, and Dino Sejdinovic · 2021
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Neuralef: Deconstructing kernels by deep neural networks
Zhijie Deng, Jiaxin Shi, and Jun Zhu · 2022
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