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Recent efforts on combining deep models with probabilistic graphical models are promising in providing flexible models that are also easy to interpret.
Parameter estimation for linear dynamical systems
Zoubin Ghahramani and Geoffrey E Hinton · 1996
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Variational learning for switching state-space models
Zoubin Ghahramani and Geoffrey E Hinton · 2000
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T. Minka · 2001
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John Winn and Christopher M Bishop · 2005
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Numerical solution of implicitly constrained optimization problems
Matthias Heinkenschloss · 2008
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A. Honkela, T. Raiko, M. Kuusela, M. Tornio, and J. Karhunen · 2011
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Matthew D Hoffman, David M Blei, Chong Wang, and John Paisley · 2013
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Sequential neural models with stochastic layers
Marco Fraccaro, Søren Kaae Sønderby, Ulrich Paquet, and Ole Winther · 2016
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Composing graphical models with neural networks for structured representations and fast inference
Matthew Johnson, David K Duvenaud, Alex Wiltschko, Ryan P Adams, and Sandeep R Datta · 2016
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Conjugate-computation variational inference: Converting variational inference in non-conjugate models to inferences in conjugate models
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Juho Kokkala, Arno Solin, and Simo Särkkä · 2015
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Rahul G Krishnan, Uri Shalit, and David Sontag · 2017
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