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[1] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[2] Joshua B. Tenenbaum,et al. One-shot learning by inverting a compositional causal process , 2013, NIPS.
[3] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[4] Peter L. Bartlett,et al. Functional Gradient Techniques for Combining Hypotheses , 2000 .
[5] Gunnar Rätsch,et al. Boosting Variational Inference: an Optimization Perspective , 2017, AISTATS.
[6] Max Welling,et al. VAE with a VampPrior , 2017, AISTATS.
[7] Andrew Y. Ng,et al. Reading Digits in Natural Images with Unsupervised Feature Learning , 2011 .
[8] Gunnar Rätsch,et al. Boosting Black Box Variational Inference , 2018, NeurIPS.
[9] Chong Wang,et al. Stochastic variational inference , 2012, J. Mach. Learn. Res..
[10] Mingyuan Zhou,et al. Semi-Implicit Variational Inference , 2018, ICML.
[11] O. Zobay. Variational Bayesian inference with Gaussian-mixture approximations , 2014 .
[12] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[13] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[14] Sergey Levine,et al. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.
[15] Evgeny Burnaev,et al. BooVAE: A scalable framework for continual VAE learning under boosting approach , 2019, ArXiv.
[16] Trevor Campbell,et al. Universal Boosting Variational Inference , 2019, NeurIPS.
[17] Gunhee Kim,et al. Variational Laplace Autoencoders , 2019, ICML.
[18] Xiangyu Wang,et al. Boosting Variational Inference , 2016, ArXiv.
[19] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[20] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[21] Max Welling,et al. Improved Variational Inference with Inverse Autoregressive Flow , 2016, NIPS 2016.
[22] Ryan P. Adams,et al. Variational Boosting: Iteratively Refining Posterior Approximations , 2016, ICML.
[23] Ruslan Salakhutdinov,et al. Importance Weighted Autoencoders , 2015, ICLR.
[24] Xiaogang Wang,et al. Deep Learning Face Attributes in the Wild , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[25] M. Friedman. Greedy Fun tion Approximation : A Gradient Boosting , 1999 .
[26] David Duvenaud,et al. Inference Suboptimality in Variational Autoencoders , 2018, ICML.
[27] Matthew D. Hoffman,et al. On the challenges of learning with inference networks on sparse, high-dimensional data , 2017, AISTATS.
[28] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[29] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[30] Peter W. Glynn,et al. Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning , 2019, ICML.
[31] Martin Jaggi,et al. Revisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization , 2013, ICML.
[32] Alexander M. Rush,et al. Semi-Amortized Variational Autoencoders , 2018, ICML.
[33] Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.
[34] Max Welling,et al. Improving Variational Auto-Encoders using Householder Flow , 2016, ArXiv.
[35] Yisong Yue,et al. Iterative Amortized Inference , 2018, ICML.