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Conventional Bayesian Neural Networks (BNNs) are unable to leverage unlabelled data to improve their predictions.
Data-Efficient Image Recognition with Contrastive Predictive Coding, July 2020
Olivier J. Hénaff, Aravind Srinivas, Jeffrey De Fauw, Ali Razavi, Carl Doersch, S. M. Ali Eslami, and Aaron van den Oord · 1905
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A useful theorem for nonlinear devices having gaussian inputs
Robert Price · 1958
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Self-organizing neural network that discovers surfaces in random-dot stereograms
Suzanna Becker and Geoffrey E. Hinton · 1992
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Bayesian Methods for Adaptive Models
David J C Mackay · 1992
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Bayesian data analysis
Andrew Gelman, John B Carlin, Hal S Stern, and Donald B Rubin · 1995
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BAYESIAN LEARNING FOR NEURAL NETWORKS
Radford M Neal · 1995
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Effective training of a neural network character classifier for word recognition
Larry Yaeger, Richard Lyon, and Brandyn Webb · 1996
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On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes
Andrew Ng and Michael Jordan · 2001
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A Simple Framework for Contrastive Learning of Visual Representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2002
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How Good is the Bayes Posterior in Deep Neural Networks Really?
Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, and Sebastian Nowozin · 2002
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The tradeoff between generative and discriminative classifiers
Guillaume Bouchard and Bill Triggs · 2004
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Big Self-Supervised Models are Strong Semi-Supervised Learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey Hinton · 2006
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Bootstrap your own latent: A new approach to self-supervised Learning, September 2020
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal Valko · 2006
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Gaussian processes for machine learning , volume 2
Christopher KI Williams and Carl Edward Rasmussen · 2006
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Strictly proper scoring rules, prediction, and estimation
Tilmann Gneiting and Adrian E Raftery · 2007
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Stoil Ganev and Laurence Aitchison · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Marginalized neural network mixtures for large-scale regression
Miguel Lázaro-Gredilla and Aníbal R Figueiras-Vidal · 2010
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Exploring Simple Siamese Representation Learning, November 2020
Xinlei Chen and Kaiming He · 2011
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Bayesian active learning for classification and preference learning
Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, and Máté Lengyel · 2011
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Machine learning: a probabilistic perspective
Kevin P Murphy · 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Semi-Supervised Learning with Deep Generative Models, October 2014
Diederik P. Kingma, Danilo J. Rezende, Shakir Mohamed, and Max Welling · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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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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Deep Kernel Learning, November 2015
Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, and Eric P. Xing · 2015
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Stochastic variational deep kernel learning
Andrew G Wilson, Zhiting Hu, Russ R Salakhutdinov, and Eric P Xing · 2016
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Mapping gaussian process priors to bayesian neural networks
Daniel Flam-Shepherd, James Requeima, and David Duvenaud · 2017
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Deep bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Xiaochun Cao, Abbas Khosravi, U Rajendra Acharya, et al · 2021
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InfoNCE is a variational autoencoder, July 2021
Laurence Aitchison · 2021
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Repulsive deep ensembles are bayesian
Francesco D’Angelo and Vincent Fortuin · 2021
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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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The ridgelet prior: A covariance function approach to prior specification for bayesian neural networks
Takuo Matsubara, Chris J Oates, and François-Xavier Briol · 2021
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Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Bayesian compression for deep learning
Christos Louizos, Karen Ullrich, and Max Welling · 2017
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Scaling sgd batch size to 32k for imagenet training
Yang You, Igor Gitman, and Boris Ginsburg · 2017
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Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum, Fabio Viola, Danilo J. Rezende, S. M. Ali Eslami, and Yee Whye Teh · 2018
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Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Dan Hendrycks and Thomas Dietterich · 2018
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On priors for Bayesian neural networks
Eric Thomas Nalisnick · 2018
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Learning invariances using the marginal likelihood
Mark van der Wilk, Matthias Bauer, ST John, and James Hensman · 2018
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Seth Nabarro, Stoil Ganev, Adrià Garriga-Alonso, Vincent Fortuin, Mark van der Wilk, and Laurence Aitchison · 2021
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Predictive complexity priors
Eric Nalisnick, Jonathan Gordon, and José Miguel Hernández-Lobato · 2021
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Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect
Lorenzo Noci, Kevin Roth, Gregor Bachmann, Sebastian Nowozin, and Thomas Hofmann · 2021
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Global inducing point variational posteriors for bayesian neural networks and deep gaussian processes
Sebastian W Ober and Laurence Aitchison · 2021
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Pacoh: Bayes-optimal meta-learning with pac-guarantees
Jonas Rothfuss, Vincent Fortuin, Martin Josifoski, and Andreas Krause · 2021
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Understanding the Behaviour of Contrastive Loss
Feng Wang and Huaping Liu · 2021
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Priors in bayesian deep learning: A review
Vincent Fortuin · 2022
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Invariance learning in deep neural networks with differentiable laplace approximations
Alexander Immer, Tycho van der Ouderaa, Gunnar Rätsch, Vincent Fortuin, and Mark van der Wilk · 2022
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Bayesian-Torch: Bayesian neural network layers for uncertainty estimation, January 2022
Ranganath Krishnan, Pi Esposito, and Mahesh Subedar · 2022
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Gedi: Generative and discriminative training for self-supervised learning
Emanuele Sansone and Robin Manhaeve · 2022
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Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors, May 2022
Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann LeCun, and Andrew Gordon Wilson · 2022
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Contrastive Learning Inverts the Data Generating Process, April 2022
Roland S. Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, and Wieland Brendel · 2022
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Variational bayesian last layers
James Harrison, John Willes, and Jasper Snoek · 2023
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Function-space regularization for deep bayesian classification
Jihao Andreas Lin, Joe Watson, Pascal Klink, and Jan Peters · 2023
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Learning instance-specific augmentations by capturing local invariances
Ning Miao, Tom Rainforth, Emile Mathieu, Yann Dubois, Yee Whye Teh, Adam Foster, and Hyunjik Kim · 2023
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Incorporating functional summary information in bayesian neural networks using a dirichlet process likelihood approach
Vishnu Raj, Tianyu Cui, Markus Heinonen, and Pekka Marttinen · 2023
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Function-space regularization in neural networks: A probabilistic perspective
Tim GJ Rudner, Sanyam Kapoor, Shikai Qiu, and Andrew Gordon Wilson · 2023
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Do bayesian neural networks need to be fully stochastic?
Mrinank Sharma, Sebastian Farquhar, Eric Nalisnick, and Tom Rainforth · 2023
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Rayen Dhahri, Alexander Immer, Betrand Charpentier, Stephan Günnemann, and Vincent Fortuin · 2024
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On the challenges and opportunities in generative ai
Laura Manduchi, Kushagra Pandey, Robert Bamler, Ryan Cotterell, Sina Däubener, Sophie Fellenz, Asja Fischer, Thomas Gärtner, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric Nalisnick, Björn Ommer, Rajesh Ranganath, Maja Rudolph, Karen Ullrich, Guy Van den Broeck, Julia E Vogt, Yixin Wang, Florian Wenzel, Frank Wood, Stephan Mandt, and Vincent Fortuin · 2024
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Position paper: Bayesian deep learning in the age of large-scale ai
Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A Osborne, Tim GJ Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, and Ruqi Zhang · 2024
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