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Variational inference (VI) has become the method of choice for fitting many modern probabilistic models.
A case study of incremental concept induction
Jeffrey C. Schlimmer and Douglas Fisher · 1986
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Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J. Cohen · 1989
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Training multilayer perceptrons with the extended Kalman algorithm
Sharad Singhal and Lance Wu · 1989
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Connectionist models of recognition memory: Constraints imposed by learning and forgetting functions
Roger Ratcliff · 1990
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Keeping the neural networks simple by minimizing the description length of the weights
Geoffrey E Hinton and Drew Van Camp · 1993
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Bayesian learning via stochastic dynamics
Radford M. Neal · 1993
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Online learning with random representations
Richard S. Sutton and Steven D. Whitehead · 1993
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Ensemble learning in Bayesian neural networks
David Barber and Christopher M. Bishop · 1998
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Improving the mean field approximation via the use of mixture distributions
Tommi S Jaakkola and Michael I Jordan · 1998
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Online learning in neural networks
Manfred Opper · 1998
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Sequential Monte Carlo methods to train neural network models
Nando de Freitas, Mahesan Niranjan, Andrew H. Gee, and Arnaud Doucet · 2000
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Online variational Bayesian learning
Zoubin Ghahramani and H. Attias · 2000
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A Bayesian committee machine
Volker Tresp · 2000
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Expectation propagation for approximate Bayesian inference
Thomas P Minka · 2001
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Online model selection based on the variational Bayes
Masa-Aki Sato · 2001
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Sparse online Gaussian processes
Lehel Csató and Manfred Opper · 2002
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Variational algorithms for approximate Bayesian inference
Matthew James Beal · 2003
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Latent dirichlet allocation
David M Blei, Andrew Y Ng, and Michael I Jordan · 2003
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Information theory, inference and learning algorithms
David JC MacKay · 2003
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Gaussian process latent variable models for visualisation of high dimensional data
Neil D Lawrence · 2004
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Power EP
Thomas P. Minka · 2004
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Laplace propagation
Alex J. Smola, S.V.N. Vishwanathan, and Eleazar Eskin · 2004
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Lack of consistency of mean field and variational Bayes approximations for state space models
Bo Wang and DM Titterington · 2004
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A unifying view of sparse approximate Gaussian process regression
Joaquin Quiñonero-Candela and Carl E. Rasmussen · 2005
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Variational message passing
John Winn, Christopher M. Bishop, and Tommi Jaakkola · 2005
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Gaussian Processes for Machine Learning
Carl E. Rasmussen and Christopher K. I. Williams · 2006
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Local Gaussian process regression for real time online model learning
Duy Nguyen-Tuong, Jan R Peters, and Matthias Seeger · 2009
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Variational learning of inducing variables in sparse Gaussian processes
Michalis K. Titsias · 2009
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Probabilistic programming with Infer.NET
John Winn and Tom Minka · 2009
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Efficient reinforcement learning using Gaussian processes
Marc Peter Deisenroth · 2010
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Approximate Riemannian conjugate gradient learning for fixed-form variational bayes
Antti Honkela, Tapani Raiko, Mikael Kuusela, Matti Tornio, and Juha Karhunen · 2010
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Nonparametric belief propagation
Erik B Sudderth, Alexander T Ihler, Michael Isard, William T Freeman, and Alan S Willsky · 2010
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Practical variational inference for neural networks
Alex Graves · 2011
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Non-conjugate variational message passing for multinomial and binary regression
David A. Knowles and Tom Minka · 2011
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Two problems with variational expectation maximisation for time-series models
Richard E. Turner and Maneesh Sahani · 2011
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Large scale distributed deep networks
Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Andrew Senior, Paul Tucker, Ke Yang, Quoc V Le, et al · 2012
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Fast variational inference in the conjugate exponential family
James Hensman, Magnus Rattray, and Neil D. Lawrence · 2012
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Bayesian learning for neural networks , volume 118
Radford M Neal · 2012
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Gaussian process occupancy maps
Simon T O’Callaghan and Fabio T Ramos · 2012
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Deep learning with elastic averaging SGD
Sixin Zhang, Anna E Choromanska, and Yann LeCun · 2015
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Tensorflow: a system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
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Revisiting distributed synchronous SGD
Jianmin Chen, Xinghao Pan, Rajat Monga, Samy Bengio, and Rafal Jozefowicz · 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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Black-box α \alpha -divergence minimization
José Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland, Daniel Hernández-Lobato, Thang D. Bui, and Richard E. Turner · 2016
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Lecture 6.5—RmsProp: Divide the gradient by a running average of its recent magnitude
Tijmen Tieleman and Geoffrey E Hinton · 2012
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Streaming variational Bayes
Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C. Wilson, and Michael I. Jordan · 2013
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Gaussian processes for big data
James Hensman, Nicolo Fusi, and Neil D Lawrence · 2013
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Stochastic variational inference
Matthew D Hoffman, David M Blei, Chong Wang, and John Paisley · 2013
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Fixed-form variational posterior approximation through stochastic linear regression
Tim Salimans and David A Knowles · 2013
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Fixed-form variational posterior approximation through stochastic linear regression
Tim Salimans, David A Knowles, et al · 2013
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Faster stochastic variational inference using proximal-gradient methods with general divergence functions
Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin, Mark Schmidt, and Masashi Sugiyama · 2016
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2016
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On sparse variational methods and the Kullback-Leibler divergence between stochastic processes
Alexander G. D. G. Matthews, James Hensman, Richard E Turner, and Zoubin Ghahramani · 2016
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Hierarchical variational models
Rajesh Ranganath, Dustin Tran, and David Blei · 2016
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Bayes and big data: The consensus Monte Carlo algorithm
Steven L Scott, Alexander W Blocker, Fernando V Bonassi, Hugh A Chipman, Edward I George, and Robert E McCulloch · 2016
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Practical secure aggregation for privacy-preserving machine learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
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Distributed bayesian learning with stochastic natural gradient expectation propagation and the posterior server
Leonard Hasenclever, Stefan Webb, Thibaut Lienart, Sebastian Vollmer, Balaji Lakshminarayanan, Charles Blundell, and Yee Whye Teh · 2017
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell · 2017
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Automatic differentiation variational inference
Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, and David M Blei · 2017
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Core50: a new dataset and benchmark for continuous object recognition
Vincenzo Lomonaco and Davide Maltoni · 2017
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GPflow: A Gaussian process library using TensorFlow
Alexander G De G Matthews, Mark Van Der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo León-Villagrá, Zoubin Ghahramani, and James Hensman · 2017
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks
Lars Mescheder, Sebastian Nowozin, and Andreas Geiger · 2017
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Ray: A distributed framework for emerging AI applications
Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, William Paul, Michael I Jordan, and Ion Stoica · 2017
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Continual learning in generative adversarial nets
Ari Seff, Alex Beatson, Daniel Suo, and Han Liu · 2017
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Continual learning with deep generative replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim · 2017
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Machine learning: The power and promise of computers that learn by example
The Royal Society · 2017
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Deep probabilistic programming
Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, and David M. Blei · 2017
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Riemannian walk for incremental learning: Understanding forgetting and intransigence
Arslan Chaudhry, Puneet K Dokania, Thalaiyasingam Ajanthan, and Philip HS Torr · 2018
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Comparing continual task learning in minds and machines
Timo Flesch, Jan Balaguer, Ronald Dekker, Hamed Nili, and Christopher Summerfield · 2018
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New metrics and experimental paradigms for continual learning
Tyler L Hayes, Ronald Kemker, Nathan D Cahill, and Christopher Kanan · 2018
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Conjugate-computation variational inference : Converting variational inference in non-conjugate models to inferences in conjugate models
Mohammad Emtiyaz Khan and Wu Lin · 2018
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Variational continual learning
Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, and Richard E. Turner · 2018
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Natural gradients in practice: Non-conjugate variational inference in Gaussian process models
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Bayesian gradient descent: Online variational Bayes learning with increased robustness to catastrophic forgetting and weight pruning, 2018
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