Implicit Reparameterization Gradients
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Shakir Mohamed | Andriy Mnih | Mikhail Figurnov | A. Mnih | S. Mohamed | Michael Figurnov | Mikhail Figurnov
[1] G. P. Bhattacharjee,et al. The Incomplete Gamma Integral , 1970 .
[2] Geoffrey W. Hill,et al. Algorithm 518: Incomplete Bessel Function I0. The Von Mises Distribution [S14] , 1977, TOMS.
[3] N. Fisher,et al. Efficient Simulation of the von Mises Distribution , 1979 .
[4] R. J. Moore. Algorithm AS 187: Derivatives of the Incomplete Gamma Integral , 1982 .
[5] L. Devroye. Non-Uniform Random Variate Generation , 1986 .
[6] R. Suri,et al. Perturbation analysis gives strongly consistent sensitivity estimates for the M/G/ 1 queue , 1988 .
[7] Peter W. Glynn,et al. Likelihood ratio gradient estimation for stochastic systems , 1990, CACM.
[8] George Marsaglia,et al. A simple method for generating gamma variables , 2000, TOMS.
[9] Michael I. Jordan,et al. Bayesian parameter estimation via variational methods , 2000, Stat. Comput..
[10] Eric Jones,et al. SciPy: Open Source Scientific Tools for Python , 2001 .
[11] Paul Glasserman,et al. Monte Carlo Methods in Financial Engineering , 2003 .
[12] Michael I. Jordan,et al. Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..
[13] Yiming Yang,et al. RCV1: A New Benchmark Collection for Text Categorization Research , 2004, J. Mach. Learn. Res..
[14] Ronald J. Williams,et al. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.
[15] John D. Lafferty,et al. Correlated Topic Models , 2005, NIPS.
[16] Yee Whye Teh,et al. A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation , 2006, NIPS.
[17] John D. Lafferty,et al. Dynamic topic models , 2006, ICML.
[18] William H. Press,et al. Numerical Recipes 3rd Edition: The Art of Scientific Computing , 2007 .
[19] Andrew McCallum,et al. Rethinking LDA: Why Priors Matter , 2009, NIPS.
[20] Francis R. Bach,et al. Online Learning for Latent Dirichlet Allocation , 2010, NIPS.
[21] Petr Sojka,et al. Software Framework for Topic Modelling with Large Corpora , 2010 .
[22] Yoshua Bengio,et al. Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.
[23] S. R. Jammalamadaka,et al. Directional Statistics, I , 2011 .
[24] Michael I. Jordan,et al. Variational Bayesian Inference with Stochastic Search , 2012, ICML.
[25] Tim Salimans,et al. Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression , 2012, ArXiv.
[26] Chong Wang,et al. Stochastic variational inference , 2012, J. Mach. Learn. Res..
[27] L. Rüschendorf. Copulas, Sklar’s Theorem, and Distributional Transform , 2013 .
[28] Karol Gregor,et al. Neural Variational Inference and Learning in Belief Networks , 2014, ICML.
[29] Miguel Lázaro-Gredilla,et al. Doubly Stochastic Variational Bayes for non-Conjugate Inference , 2014, ICML.
[30] Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.
[31] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[32] Sean Gerrish,et al. Black Box Variational Inference , 2013, AISTATS.
[33] David A. Knowles. Stochastic gradient variational Bayes for gamma approximating distributions , 2015, 1509.01631.
[34] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[35] Julien Cornebise,et al. Weight Uncertainty in Neural Networks , 2015, ArXiv.
[36] David M. Blei,et al. Stochastic Structured Variational Inference , 2014, AISTATS.
[37] Zoubin Ghahramani,et al. A Theoretically Grounded Application of Dropout in Recurrent Neural Networks , 2015, NIPS.
[38] Ruslan Salakhutdinov,et al. Importance Weighted Autoencoders , 2015, ICLR.
[39] Yuan Yu,et al. TensorFlow: A system for large-scale machine learning , 2016, OSDI.
[40] David M. Blei,et al. The Generalized Reparameterization Gradient , 2016, NIPS.
[41] Alex Graves,et al. Stochastic Backpropagation through Mixture Density Distributions , 2016, ArXiv.
[42] Zoubin Ghahramani,et al. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.
[43] Lars Hertel,et al. Approximate Inference for Deep Latent Gaussian Mixtures , 2016 .
[44] Scott W. Linderman,et al. Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms , 2016, AISTATS.
[45] Charles A. Sutton,et al. Autoencoding Variational Inference For Topic Models , 2017, ICLR.
[46] Marco Cote. STICK-BREAKING VARIATIONAL AUTOENCODERS , 2017 .
[47] Ben Poole,et al. Categorical Reparameterization with Gumbel-Softmax , 2016, ICLR.
[48] Dustin Tran,et al. Automatic Differentiation Variational Inference , 2016, J. Mach. Learn. Res..
[49] Dmitry P. Vetrov,et al. Variational Dropout Sparsifies Deep Neural Networks , 2017, ICML.
[50] Yee Whye Teh,et al. The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables , 2016, ICLR.
[51] Luca Antiga,et al. Automatic differentiation in PyTorch , 2017 .
[52] Kristopher L. Kuhlman,et al. mpmath: a Python library for arbitrary-precision floating-point arithmetic , 2017 .
[53] Barak A. Pearlmutter,et al. Automatic differentiation in machine learning: a survey , 2015, J. Mach. Learn. Res..
[54] Dustin Tran,et al. TensorFlow Distributions , 2017, ArXiv.
[55] David Duvenaud,et al. Sticking the Landing: An Asymptotically Zero-Variance Gradient Estimator for Variational Inference , 2017, ArXiv.
[56] Charles A. Sutton,et al. Variational Inference In Pachinko Allocation Machines , 2018, ArXiv.
[57] Hao Zhang,et al. WHAI: Weibull Hybrid Autoencoding Inference for Deep Topic Modeling , 2018, ICLR.
[58] Nicola De Cao,et al. Hyperspherical Variational Auto-Encoders , 2018, UAI 2018.
[59] Martin Jankowiak,et al. Pathwise Derivatives Beyond the Reparameterization Trick , 2018, ICML.
[60] Theofanis Karaletsos,et al. Pathwise Derivatives for Multivariate Distributions , 2018, AISTATS.