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[1] S. M. Ali,et al. A General Class of Coefficients of Divergence of One Distribution from Another , 1966 .
[2] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[3] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[4] Radford M. Neal. Annealed importance sampling , 1998, Stat. Comput..
[5] Trevor Hastie,et al. The elements of statistical learning. 2001 , 2001 .
[6] Eric R. Ziegel,et al. The Elements of Statistical Learning , 2003, Technometrics.
[7] John K Kruschke,et al. Bayesian data analysis. , 2010, Wiley interdisciplinary reviews. Cognitive science.
[8] Martin J. Wainwright,et al. Estimating Divergence Functionals and the Likelihood Ratio by Convex Risk Minimization , 2008, IEEE Transactions on Information Theory.
[9] Z. Szabó. Information Theoretical Estimators (ITE) Toolbox , 2013, NIPS 2013.
[10] Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.
[11] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[12] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[13] Shakir Mohamed,et al. Variational Inference with Normalizing Flows , 2015, ICML.
[14] Xiaogang Wang,et al. Deep Learning Face Attributes in the Wild , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[15] Max Welling,et al. Markov Chain Monte Carlo and Variational Inference: Bridging the Gap , 2014, ICML.
[16] Andrew Gelman,et al. Automatic Variational Inference in Stan , 2015, NIPS.
[17] Yoshua Bengio,et al. NICE: Non-linear Independent Components Estimation , 2014, ICLR.
[18] Alex Graves,et al. Conditional Image Generation with PixelCNN Decoders , 2016, NIPS.
[19] Qiang Liu. Wild Variational Approximations , 2016 .
[20] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Koray Kavukcuoglu,et al. Pixel Recurrent Neural Networks , 2016, ICML.
[22] Theofanis Karaletsos,et al. Adversarial Message Passing For Graphical Models , 2016, ArXiv.
[23] Ole Winther,et al. Auxiliary Deep Generative Models , 2016, ICML.
[24] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[25] Dustin Tran,et al. Operator Variational Inference , 2016, NIPS.
[26] Jascha Sohl-Dickstein,et al. Improved generator objectives for GANs , 2016, ArXiv.
[27] Max Welling,et al. Improving Variational Auto-Encoders using Householder Flow , 2016, ArXiv.
[28] Ole Winther,et al. Autoencoding beyond pixels using a learned similarity metric , 2015, ICML.
[29] Dustin Tran,et al. Variational Gaussian Process , 2015, ICLR.
[30] Sebastian Nowozin,et al. f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization , 2016, NIPS.
[31] Samy Bengio,et al. Density estimation using Real NVP , 2016, ICLR.
[32] Yoshua Bengio,et al. Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[33] Trevor Darrell,et al. Adversarial Feature Learning , 2016, ICLR.
[34] Ferenc Huszár,et al. Variational Inference using Implicit Distributions , 2017, ArXiv.
[35] Pieter Abbeel,et al. Variational Lossy Autoencoder , 2016, ICLR.
[36] Max Welling,et al. Improved Variational Inference with Inverse Autoregressive Flow , 2016, NIPS 2016.
[37] Ruslan Salakhutdinov,et al. On the Quantitative Analysis of Decoder-Based Generative Models , 2016, ICLR.
[38] Qiang Liu,et al. Two Methods for Wild Variational Inference , 2016, 1612.00081.
[39] Aaron C. Courville,et al. Adversarially Learned Inference , 2016, ICLR.