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Probabilistic graphical models are powerful tools which allow us to formalise our knowledge about the world and reason about its inherent uncertainty.
Monte carlo sampling methods using markov chains and their applications
W Keith Hastings · 1970
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Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell Stinchcombe, and Halbert White · 1989
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A tractable inference algorithm for diagnosing multiple diseases
David Heckerman · 1990
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An empirical analysis of likelihood-weighting simulation on a large, multiply connected medical belief network
Michael Shwe and Gregory Cooper · 1991
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Variational probabilistic inference and the QMR-DT network
Tommi S Jaakkola and Michael I Jordan · 1999
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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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Loopy belief propagation for approximate inference: An empirical study
Kevin P Murphy, Yair Weiss, and Michael I Jordan · 1999
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AIS-BN: An adaptive importance sampling algorithm for evidential reasoning in large Bayesian networks
Jian Cheng and Marek J. Druzdzel · 2000
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Approximate inference algorithms for two-layer bayesian networks
Andrew Y Ng and Michael I Jordan · 2000
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Recognition networks for approximate inference in bn20 networks
Quaid Morris · 2001
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Annealed importance sampling
Radford M Neal · 2001
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Stable fixed points of loopy belief propagation are local minima of the bethe free energy
Tom Heskes · 2003
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Inferring cellular networks using probabilistic graphical models
Nir Friedman · 2004
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Liblinear: A library for large linear classification
Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, and Chih-Jen Lin · 2008
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Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
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Graphical models, exponential families, and variational inference
Martin J Wainwright, Michael I Jordan, et al · 2008
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Neural variational inference and learning in belief networks
Andriy Mnih and Karol Gregor · 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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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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MADE: masked autoencoder for distribution estimation
Mathieu Germain, Karol Gregor, Iain Murray, and Hugo Larochelle · 2015
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Probabilistic graphical models: principles and techniques
Daphne Koller and Nir Friedman · 2009
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Pierre Baldi and Peter Sadowski · 2014
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Amortized inference in probabilistic reasoning
Samuel Gershman and Noah Goodman · 2014
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Neural adaptive sequential monte carlo
Shixiang Gu, Zoubin Ghahramani, and Richard E Turner · 2015
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Inference networks for sequential Monte Carlo in graphical models
Brooks Paige and Frank Wood · 2016
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Deep amortized inference for probabilistic programs
Daniel Ritchie, Paul Horsfall, and Noah D Goodman · 2016
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Using synthetic data to train neural networks is model-based reasoning
Tuan Anh Le, Atilim Gunes Baydin, Robert Zinkov, and Frank Wood · 2017
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