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Variational autoencoder (VAE) is a very successful generative model whose key element is the so called amortized inference network, which can perform test time inference using a single feed forward pass.
Bayesian variational autoencoders for unsupervised out-of-distribution detection
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A unifying view of sparse approximate Gaussian process regression
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Variational learning of inducing variables in sparse Gaussian processes, 2009
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Reading digits in natural images with unsupervised feature learning
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Imagenet classification with deep convolutional neural networks, 2012
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Stochastic variational inference
Hoffman, M. D., Blei, D. M., Wang, C., and Paisley, J · 2013
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One-shot learning by inverting a compositional causal process, 2013
Lake, B. M., Salakhutdinov, R. R., and Tenenbaum, J · 2013
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2013
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Auto-encoding variational Bayes, 2014
Kingma, D. P. and Welling, M · 2014
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Stochastic backpropagation and approximate inference in deep generative models, 2014
Rezende, D., Mohamed, S., and Wierstra, D · 2014
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Scalable inference for Gaussian process models with black-box likelihoods, 2015
Dezfouli, A. and Bonilla, E. V · 2015
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Scalable Gaussian process regression using deep neural networks, 2015
Huang, W., Zhao, D., Sun, F., Liu, H., and Chang, E · 2015
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Variational inference for Gaussian process modulated Poisson processes
Lloyd, C., Gunter, T., Osborne, M. A., and Roberts, S. J · 2015
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Variational Fourier features for Gaussian processes
Hensman, J., Durrande, N., and Solin, A · 2017
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Inference suboptimality in variational autoencoders
Cremer, C., Li, X., and Duvenaud, D · 2018
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Gaussian process behaviour in wide deep neural networks, 2018
de G. Matthews, A. G., Hron, J., Rowland, M., Turner, R. E., and Ghahramani, Z · 2018
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Semi-amortized variational autoencoders
Kim, Y., Wiseman, S., Millter, A. C., Sontag, D., and Rush, A. M · 2018
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On the challenges of learning with inference networks on sparse high-dimensional data
Krishnan, R. G., Liang, D., and Hoffman, M. D · 2018
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Deep neural networks as gaussian processes, 2018
Lee, J., Bahri, Y., Novak, R., Schoenholz, S. S., Pennington, J., and Sohl-Dickstein, J · 2018
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Unsupervised representation learning with deep convolutional generative adversarial networks
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Importance weighted autoencoders, 2016
Burda, Y., Grosse, R., and Salakhutdinov, R · 2016
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Improving variational inference with inverse autoregressive flow, 2016
Kingma, D. P., Salimans, T., Jozefowicz, R., Chen, X., Sutskever, I., and Welling, M · 2016
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Improving variational autoencoders using Householder flow, 2016
Tomczak, J. M. and Welling, M · 2016
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Wilson, A. G., Hu, Z., Salakhutdinov, R., and Xing, E. P · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
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Iterative amortized inference
Marino, J., Yisong, Y., and Mandt, S · 2018
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Deep convolutional networks as shallow Gaussian processes, 2019
Garriga-Alonso, A., Rasmussen, C. E., and Aitchison, L · 2019
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Variational Laplace autoencoders
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Compound variational auto-encoder, 2019
Su, S.-Y., Lin, S.-W., and Chen, Y.-N · 2019
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Recursive inference for variational autoencoders
Kim, M. and Pavlovic, V · 2020
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Functional regularisation for continual learning with gaussian processes, 2020
Titsias, M. K., Schwarz, J., de G. Matthews, A. G., Pascanu, R., and Teh, Y. W · 2020
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