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Probabilistic models are often trained by maximum likelihood, which corresponds to minimizing a specific f-divergence between the model and data distribution.
Asymptotic Efficiency of the Maximum Likelihood Estimator
Wolfowitz, J · 1965
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An Auxiliary Variational Method
Agakov, F. V. and Barber, D · 2004
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Minka, T · 2005
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MNIST Handwritten Digit Database
LeCun, Y. and Cortes, C · 2010
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Estimating Divergence Functionals and the Likelihood Ratio by Convex Risk Minimization
Nguyen, X., Wainwright, M. J., and Jordan, M. I · 2010
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Rényi Divergence and Kullback-Leibler Divergence
van Erven, T. and Harremoës, P · 2012
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Auto-Encoding Variational Bayes
Kingma, D. P. and Welling, M · 2013
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Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Adam: A Method for Stochastic Optimization
Kingma, D. P. and Ba, J · 2014
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Stochastic Backpropagation and Approximate Inference in Deep Generative Models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
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Poole, B., Alemi, A. A., Sohl-Dickstein, J., and Angelova, A · 2016
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Complementary Sum Sampling For Likelihood Approximation in Large Scale Classification
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Variational Inference using Implicit Distributions
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Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks
Mescheder, L., Nowozin, S., and Geiger, A · 2017
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NIPS 2016 Tutorial: Generative Adversarial Networks
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Mohamed, S. and Lakshminarayanan, B · 2016
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Stabilizing Training of Generative Adversarial Networks through Regularization
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Amortised MAP Inference for Image Super-Resolution
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Ruiz, F., Titsias, M., Dieng, A., and Blei, D · 2018
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Spread divergence
Zhang, M., Hayes, P., Bird, T., Habib, R., and Barber, D · 2020
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