M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng. TensorFlow: Large-scale machine learning on heterogeneous systems, 2015. Software available from tensorflow.org.
 E. Archer, I. M. Park, L. Buesing, J. Cunningham, and L. Paninski. Black box variational inference for state space models. arXiv:1511.07367, 2015.
 P. W. Battaglia, R. Pascanu, M. Lai, D. J. Rezende, and K. Kavukcuoglu. Interaction networks for learning about objects, relations and physics. In NIPS, 2016. [OpenAIRE]
 M. B. Chang, T. Ullman, A. Torralba, and J. B. Tenenbaum. A compositional object-based approach to learning physical dynamics. In ICLR, 2017.
 S. Chiappa, S. Racanière, D. Wierstra, and S. Mohamed. Recurrent environment simulators. In ICLR, 2017. [OpenAIRE]
 J. Chung, K. Kastner, L. Dinh, K. Goel, A. C. Courville, and Y. Bengio. A recurrent latent variable model for sequential data. In NIPS, 2015.
 C. Finn, I. J. Goodfellow, and S. Levine. Unsupervised learning for physical interaction through video prediction. In NIPS, 2016.
 M. Fraccaro, S. K. Sønderby, U. Paquet, and O. Winther. Sequential neural models with stochastic layers. In NIPS, 2016. [OpenAIRE]
 K. Fragkiadaki, P. Agrawal, S. Levine, and J. Malik. Learning visual predictive models of physics for playing billiards. In ICLR, 2016.
 Y. Gao, E. W. Archer, L. Paninski, and J. P. Cunningham. Linear dynamical neural population models through nonlinear embeddings. In NIPS, 2016.
 T. Haarnoja, A. Ajay, S. Levine, and P. Abbeel. Backprop KF: learning discriminative deterministic state estimators. In NIPS, 2016. [OpenAIRE]
 I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. Botvinick, S. Mohamed, and A. Lerchner. beta-vae: Learning basic visual concepts with a constrained variational framework. 2017.
 S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural Computation, 9(8):1735-1780, Nov. 1997.
 E. Jang, S. Gu, and B. Poole. Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144, 2016.
 M. J. Johnson, D. Duvenaud, A. B. Wiltschko, S. R. Datta, and R. P. Adams. Composing graphical models with neural networks for structured representations and fast inference. In NIPS, 2016. [OpenAIRE]