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The reparameterization trick is widely used in variational inference as it yields more accurate estimates of the gradient of the variational objective than alternative approaches such as the score function method.
Certain generalizations in the analysis of variance
Wilks, S. S. (1932) · 1932
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The generalized Weierstrass approximation theorem
Stone, M. H. (1948) · 1948
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A stochastic approximation method
Robbins, H. and Monro, S. (1951) · 1951
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Doubly stochastic variational Bayes for non-conjugate inference
Titsias, M. and Lázaro-Gredilla, M. (2014) · 1979
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R. J. (1992) · 1992
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998) · 1998
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An introduction to variational methods for graphical models
Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., and Saul, L. K. (1999) · 1999
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Large-scale machine learning with stochastic gradient descent
Bottou, L. (2010) · 2010
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Explaining variational approximations
Ormerod, J. T. and Wand, M. P. (2010) · 2010
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Adaptive subgradient methods for online learning and stochastic optimization
Duchi, J., Hazan, E., and Singer, Y. (2011) · 2011
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Regression density estimation with variational methods and stochastic approximation
Nott, D. J., Tan, S. L., Villani, M., and Kohn, R. (2012) · 2012
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Adadelta: an adaptive learning rate method
Zeiler, M. D. (2012) · 2012
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Auto-encoding variational Bayes
Kingma, D. P. and Welling, M. (2014) · 2014
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Black box variational inference
Ranganath, R., Gerrish, S., and Blei, D. (2014) · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D. (2014) · 2014
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Variational Bayesian inference with Gaussian-mixture approximations
Zobay, O. (2014) · 2014
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Black-box stochastic variational inference in five lines of python
The generalized reparameterization gradient
Ruiz, F. R., Titsias, M. K., and Blei, D. (2016) · 2016
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Variational inference: A review for statisticians
Blei, D. M., Kucukelbir, A., and McAuliffe, J. D. (2017) · 2017
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Automatic differentiation variational inference
Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A., and Blei, D. M. (2017) · 2017
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Reducing reparameterization gradient variance
Miller, A., Foti, N., D’Amour, A., and Adams, R. P. (2017) · 2017
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Sticking the landing: Simple, lower-variance gradient estimators for variational inference
Roeder, G., Wu, Y., and Duvenaud, D. K. (2017) · 2017
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Variational Bayes with intractable likelihood
Tran, M.-N., Nott, D. J., and Kohn, R. (2017) · 2017
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Duvenaud, D. and Adams, R. P. (2015) · 2015
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Fast second order stochastic backpropagation for variational inference
Fan, K., Wang, Z., Beck, J., Kwok, J., and Heller, K. A. (2015) · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2015) · 2015
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Uncertainty in Deep Learning
Gal, Y. (2016) · 2016
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Variational Gaussian copula inference
Han, S., Liao, X., Dunson, D. B., and Carin, L. C. (2016) · 2016
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Variational boosting: Iteratively refining posterior approximations
Miller, A. C., Foti, N., and Adams, R. P. (2016) · 2016
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Variational Bayes with synthetic likelihood
Ong, V. M., Nott, D. J., Tran, M.-N., Sisson, S. A., and Drovandi, C. C. (2018a)
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Dissecting Adam: The sign, magnitude and variance of stochastic gradients
Balles, L. and Hennig, P. (2018) · 2018
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Implicit reparameterization gradients
Figurnov, M., Mohamed, S., and Mnih, A. (2018) · 2018
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Reparameterization gradient for non-differentiable models
Lee, W., Yu, H., and Yang, H. (2018) · 2018
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Gaussian variational approximation for high-dimensional state space models
Quiroz, M., Nott, D. J., and Kohn, R. (2018) · 2018
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Gaussian variational approximation with sparse precision matrices
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