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Inference models are a key component in scaling variational inference to deep latent variable models, most notably as encoder networks in variational auto-encoders (VAEs).
Maximum likelihood from incomplete data via the em algorithm
Dempster, A. P., Laird, N. M., and Rubin, D. B · 1977
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Maximum likelihood from incomplete data via the em algorithm
Dempster, A. P., Laird, N. M., and Rubin, D. B · 1977
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The helmholtz machine
Dayan, P., Hinton, G. E., Neal, R. M., and Zemel, R. S · 1995
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The helmholtz machine
Dayan, P., Hinton, G. E., Neal, R. M., and Zemel, R. S · 1995
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An introduction to variational methods for graphical models
Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., and Saul, L. K · 1998
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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A view of the em algorithm that justifies incremental, sparse, and other variants
Neal, R. M. and Hinton, G. E · 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 · 1998
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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A view of the em algorithm that justifies incremental, sparse, and other variants
Neal, R. M. and Hinton, G. E · 1998
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Rcv1: A new benchmark collection for text categorization research
Lewis, D. D., Yang, Y., Rose, T. G., and Li, F · 2004
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Rcv1: A new benchmark collection for text categorization research
Lewis, D. D., Yang, Y., Rose, T. G., and Li, F · 2004
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
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Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
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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
Lake, B. M., Salakhutdinov, R. R., and Tenenbaum, J · 2013
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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
Lake, B. M., Salakhutdinov, R. R., and Tenenbaum, J · 2013
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Amortized inference in probabilistic reasoning
Gershman, S. and Goodman, N · 2014
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Deep autoregressive networks
Gregor, K., Danihelka, I., Mnih, A., Blundell, C., and Wierstra, D · 2014
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Adam: A method for stochastic optimization
Kingma, D. and Ba, J · 2014
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Stochastic gradient vb and the variational auto-encoder
Kingma, D. P. and Welling, M · 2014
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Black box variational inference
Ranganath, R., Gerrish, S., and Blei, D · 2014
Cited alongside, same era.
Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
Cited alongside, same era.
Iterative refinement of the approximate posterior for directed belief networks
Hjelm, D., Salakhutdinov, R. R., Cho, K., Jojic, N., Calhoun, V., and Chung, J · 2016
Later among the works it cites.
Ladder variational autoencoders
Sønderby, C. K., Raiko, T., Maaløe, L., Sønderby, S. K., and Winther, O · 2016
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Visual dynamics: Probabilistic future frame synthesis via cross convolutional networks
Xue, T., Wu, J., Bouman, K., and Freeman, B · 2016
Later among the works it cites.
Learning to learn by gradient descent by gradient descent
Andrychowicz, M., Denil, M., Gomez, S., Hoffman, M. W., Pfau, D., Schaul, T., and de Freitas, N · 2016
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Ba, J. L., Kiros, J. R., and Hinton, G. E · 2016
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Towards conceptual compression
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Amortized inference in probabilistic reasoning
Gershman, S. and Goodman, N · 2014
Cited alongside, same era.
Deep autoregressive networks
Gregor, K., Danihelka, I., Mnih, A., Blundell, C., and Wierstra, D · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. and Ba, J · 2014
Cited alongside, same era.
Stochastic gradient vb and the variational auto-encoder
Kingma, D. P. and Welling, M · 2014
Cited alongside, same era.
Black box variational inference
Ranganath, R., Gerrish, S., and Blei, D · 2014
Cited alongside, same era.
Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
Cited alongside, same era.
Gregor, K., Besse, F., Rezende, D. J., Danihelka, I., and Wierstra, D · 2016
Later among the works it cites.
beta-vae: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A · 2016
Later among the works it cites.
Iterative refinement of the approximate posterior for directed belief networks
Hjelm, D., Salakhutdinov, R. R., Cho, K., Jojic, N., Calhoun, V., and Chung, J · 2016
Later among the works it cites.
Ladder variational autoencoders
Sønderby, C. K., Raiko, T., Maaløe, L., Sønderby, S. K., and Winther, O · 2016
Later among the works it cites.
Visual dynamics: Probabilistic future frame synthesis via cross convolutional networks
Xue, T., Wu, J., Bouman, K., and Freeman, B · 2016
Later among the works it cites.
Inference suboptimality in variational autoencoders
Cremer, C., Li, X., and Duvenaud, D · 2017
Later among the works it cites.
Deep variational bayes filters: Unsupervised learning of state space models from raw data
Karl, M., Soelch, M., Bayer, J., and van der Smagt, P · 2017
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Recurrent inference machines for solving inverse problems
Putzky, P. and Welling, M · 2017
Later among the works it cites.
Inference suboptimality in variational autoencoders
Cremer, C., Li, X., and Duvenaud, D · 2017
Later among the works it cites.
Deep variational bayes filters: Unsupervised learning of state space models from raw data
Karl, M., Soelch, M., Bayer, J., and van der Smagt, P · 2017
Later among the works it cites.
Recurrent inference machines for solving inverse problems
Putzky, P. and Welling, M · 2017
Later among the works it cites.
Semi-amortized variational autoencoders
Kim, Y., Wiseman, S., Miller, 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 · 2018
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Semi-amortized variational autoencoders
Kim, Y., Wiseman, S., Miller, A. C., Sontag, D., and Rush, A. M · 2018
Closest in time.
On the challenges of learning with inference networks on sparse, high-dimensional data
Krishnan, R. G., Liang, D., and Hoffman, M · 2018
Closest in time.