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Sequential Monte Carlo (SMC), or particle filtering, is a popular class of methods for sampling from an intractable target distribution using a sequence of simpler intermediate distributions.
N. J. Gordon, D. J. Salmond, and A. F. Smith, “Novel approach to nonlinear/non-gaussian bayesian state estimation,” in IEE Proceedings F (Radar and Signal Processing)
1993
Earlier work this paper cites.
C. M. Bishop, “Mixture density networks,” 1994
1994
Earlier work this paper cites.
G. E. Hinton, P. Dayan, B. J. Frey, and R. M. Neal, “The” wake-sleep” algorithm for unsupervised neural networks,” Science
1995
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation
1997
Earlier work this paper cites.
R. Van Der Merwe, A. Doucet, N. De Freitas, and E. Wan, “The unscented particle filter,” in Advances in Neural Information Processing Systems
2000
Earlier work this paper cites.
Springer-Verlag, 2001
A. Doucet, N. De Freitas, and N. Gordon, Sequential monte carlo methods in practice · 2001
Earlier work this paper cites.
T. P. Minka, “Expectation propagation for approximate bayesian inference,” in Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
2001
Earlier work this paper cites.
Cambridge university press Cambridge, 2003
D. J. MacKay, Information theory, inference, and learning algorithms · 2003
Earlier work this paper cites.
PhD thesis, Ph. D. thesis, University Pierre and Marie Curie–Paris 6, 2009
J. Cornebise, Adaptive Sequential Monte Carlo Methods · 2009
Earlier work this paper cites.
C. Andrieu, A. Doucet, and R. Holenstein, “Particle markov chain monte carlo methods,” Journal of the Royal Statistical Society: Series B (Statistical Methodology)
2010
Cited alongside, same era.
G. Poyiadjis, A. Doucet, and S. S. Singh, “Particle approximations of the score and observed information matrix in state space models with application to parameter estimation,” Biometrika
2011
Cited alongside, same era.
R. E. Turner and M. Sahani, “Two problems with variational expectation maximisation for time-series models,” in Bayesian Time series models
2011
Cited alongside, same era.
Springer, 2012
A. Graves, Supervised sequence labelling with recurrent neural networks · 2012
Cited alongside, same era.
N. Boulanger-Lewandowski, Y. Bengio, and P. Vincent, “Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription,” in International Conference on Machine Learning (ICML)
PhD thesis, University of Cambridge UK, Department of Engineering, 2014
A. McHutchon, Nonlinear modelling and control using Gaussian processes · 2014
Later among the works it cites.
J. Bayer and C. Osendorfer, “Learning stochastic recurrent networks,” arXiv preprint arXiv:1411.7610
2014
Later among the works it cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” The International Conference on Learning Representations (ICLR)
2014
Later among the works it cites.
D. J. Rezende, S. Mohamed, and D. Wierstra, “Stochastic backpropagation and approximate inference in deep generative models,” International Conference on Machine Learning (ICML)
2014
Later among the works it cites.
A. Mnih and K. Gregor, “Neural variational inference and learning in belief networks,” International Conference on Machine Learning (ICML)
2014
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2012
Cited alongside, same era.
A. Graves, “Generating sequences with recurrent neural networks,” CoRR
2013
Cited alongside, same era.
Y. Bengio, N. Boulanger-Lewandowski, and R. Pascanu, “Advances in optimizing recurrent networks,” in Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on
2013
Cited alongside, same era.
R. Frigola, Y. Chen, and C. Rasmussen, “Variational gaussian process state-space models,” in Advances in Neural Information Processing Systems
2014
Cited alongside, same era.
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in Advances in Neural Information Processing Systems
2014
Cited alongside, same era.
Later among the works it cites.
K. Gregor, I. Danihelka, A. Graves, D. J. Rezende, and D. Wierstra, “DRAW: A recurrent neural network for image generation,” in Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015
2015
Closest in time.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” The International Conference on Learning Representations (ICLR)
2015
Closest in time.