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Automatic Differentiation Variational Inference (ADVI) is a useful tool for efficiently learning probabilistic models in machine learning.
Novelty detection and neural network validation
Christopher M Bishop · 1993
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Approximating posterior distributions by mixtures
Mike West · 1993
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Approximating posterior distributions in belief networks using mixtures
Christopher M Bishop, Neil D Lawrence, Tommi Jaakkola, and Michael I Jordan · 1998
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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An introduction to variational methods for graphical models
Michael I Jordan, Zoubin Ghahramani, Tommi S Jaakkola, and Lawrence K Saul · 1999
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Fixed-form variational posterior approximation through stochastic linear regression
Tim Salimans and David A Knowles · 2013
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Auto-encoding variational bayes
P Kingma Diederik, Max Welling, et al · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 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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Importance weighted autoencoders
Yuri Burda, Roger Grosse Roger, and Ruslan Salakhutdinov · 2015
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Fast and accurate deep network learning by exponential linear units (elus)
Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter · 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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Deep unsupervised clustering with Gaussian mixture variational autoencoders
Nat Dilokthanakul, Pedro AM Mediano, Marta Garnelo, Matthew CH Lee, Hugh Salimbeni, Kai Arulkumaran, and Murray Shanahan · 2016
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Tutorial on variational autoencoders
Carl Doersch · 2016
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Stochastic backpropagation through mixture density distributions
Alex Graves · 2016
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Composing graphical models with neural networks for structured representations and fast inference
Matthew J Johnson, David K Duvenaud, Alex Wiltschko, Ryan P Adams, and Sandeep R Datta · 2016
Sticking the landing: Simple, lower-variance gradient estimators for variational inference
Geoffrey Roeder, Yuhuai Wu, and David K Duvenaud · 2017
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Jakub M Tomczak and Max Welling · 2017
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Implicit reparameterization gradients
Mikhail Figurnov, Shakir Mohamed, and Andriy Mnih · 2018
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Pathwise derivatives beyond the reparameterization trick
Martin Jankowiak and Fritz Obermeyer · 2018
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Do deep generative models know what they don’t know?
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, and Balaji Lakshminarayanan · 2018
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The generalized reparameterization gradient
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Deep variational information bottleneck
Alex Alemi, Ian Fischer, Josh Dillon, and Kevin Murphy · 2017
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Reinterpreting importance-weighted autoencoders
Chris Cremer, Quaid Morris, and David Duvenaud · 2017
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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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Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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Variational deep embedding: An unsupervised and generative approach to clustering
Zhuxi Jiang, Yin Zheng, Huachun Tan, Bangsheng Tang, and Hanning Zhou · 2017
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Tighter variational bounds are not necessarily better
Tom Rainforth, Adam R Kosiorek, Tuan Anh Le, Chris J Maddison, Maximilian Igl, Frank Wood, and Yee Whye Teh · 2018
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Divide and couple: Using Monte Carlo variational objectives for posterior approximation
Justin Domke and Daniel R Sheldon · 2019
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Modeling uncertainty with hedged instance embeddings
Seong Joon Oh, Andrew C. Gallagher, Kevin P. Murphy, Florian Schroff, Jiyan Pan, and Joseph Roth · 2019
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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Jasper Snoek, Yaniv Ovadia, Emily Fertig, Balaji Lakshminarayanan, Sebastian Nowozin, D Sculley, Joshua Dillon, Jie Ren, and Zachary Nado · 2019
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Doubly reparameterized gradient estimators for monte carlo objectives
George Tucker, Dieterich Lawson, Shixiang Gu, and Christopher Maddison · 2019
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Mixture distributions for scalable Bayesian inference, 2020
Pranav Poduval, Hrushikesh Loya, Rajat Patel, and Sumit Jain · 2020
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