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State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification.
A new approach to linear filtering and prediction problems
Kalman, R.E · 1960
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Identification and control of dynamical systems using neural networks
Narendra, Kumpati S and Parthasarathy, Kannan · 1990
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LSTM can solve hard long time lag problems
Hochreiter, Sepp and Schmidhuber, Jürgen · 1997
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System identification
Ljung, Lennart · 1998
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LWPR: An O(n) algorithm for incremental real time learning in high dimensional space
Vijayakumar, Sethu and Schaal, Stefan · 2000
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Gaussian process priors with ARMA noise models
Murray-Smith, Roderick and Girard, Agathe · 2001
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Predictive representations of state
Littman, Michael L and Sutton, Richard S · 2002
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Multiple-step ahead prediction for non linear dynamic systems—a Gaussian process treatment with propagation of the uncertainty
Girard, Agathe, Rasmussen, Carl Edward, Quinonero-Candela, J, Murray-Smith, R, Winther, O, and Larsen, J · 2003
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Learning predictive state representations
Singh, Satinder P, Littman, Michael L, Jong, Nicholas K, Pardoe, David, and Stone, Peter · 2003
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A nonlinear predictive state representation
Rudary, Matthew R and Singh, Satinder P · 2004
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Dynamic systems identification with Gaussian processes
Kocijan, Juš, Girard, Agathe, Banko, Blaž, and Murray-Smith, Roderick · 2005
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Gaussian processes for machine learning
Williams, Christopher KI and Rasmussen, Carl Edward · 2005
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Sparse Gaussian processes using pseudo-inputs
Snelson, Edward and Ghahramani, Zoubin · 2006
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Predictive control of a gas–liquid separation plant based on a Gaussian process model
Likar, Bojan and Kocijan, Juš · 2007
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Gaussian process dynamical models for human motion
Wang, Jack M, Fleet, David J, and Hertzmann, Aaron · 2008
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Gp-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models
Ko, Jonathan and Fox, Dieter · 2009
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Variational learning of inducing variables in sparse Gaussian processes
Titsias, Michalis K · 2009
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Perspectives on system identification
Ljung, Lennart · 2010
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State-space inference and learning with Gaussian processes
Turner, Ryan, Deisenroth, Marc, and Rasmussen, Carl · 2010
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Input-output data sets for development and benchmarking in nonlinear identification
Wigren, Torbjörn · 2010
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PILCO: A model-based and data-efficient approach to policy search
Deisenroth, Marc P and Rasmussen, Carl Edward · 2011
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Auto-encoding variational bayes
Kingma, Diederik P and Welling, Max · 2013
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On the difficulty of training recurrent neural networks
Pascanu, Razvan, Mikolov, Tomas, and Bengio, Yoshua · 2013
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Variational Gaussian process state-space models
Frigola, Roger, Chen, Yutian, and Rasmussen, Carl Edward · 2014
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Translating videos to natural language using deep recurrent neural networks
Venugopalan, Subhashini, Xu, Huijuan, Donahue, Jeff, Rohrbach, Marcus, Mooney, Raymond, and Saenko, Kate · 2014
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Bayesian time series learning with Gaussian processes
Frigola-Alcade, Roger · 2015
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Gaussian process training with input noise
McHutchon, Andrew and Rasmussen, Carl Edwards · 2011
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Generating text with recurrent neural networks
Sutskever, Ilya, Martens, James, and Hinton, Geoffrey E · 2011
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Robust filtering and smoothing with gaussian processes
Deisenroth, Marc Peter, Turner, Ryan Darby, Huber, Marco F, Hanebeck, Uwe D, and Rasmussen, Carl Edward · 2012
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Subspace identification for linear systems: Theory—Implementation—Applications
Van Overschee, Peter and De Moor, BL · 2012
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Nonlinear system identification: NARMAX methods in the time, frequency, and spatio-temporal domains
Billings, Stephen A · 2013
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Model predictive control
Camacho, Eduardo F and Alba, Carlos Bordons · 2013
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Mattos, César Lincoln C, Dai, Zhenwen, Damianou, Andreas, Forth, Jeremy, Barreto, Guilherme A, and Lawrence, Neil D · 2015
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Latent autoregressive Gaussian processes models for robust system identification
Mattos, César Lincoln C, Damianou, Andreas, Barreto, Guilherme A, and Lawrence, Neil D · 2016
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Variational inference: A review for statisticians
Blei, David M, Kucukelbir, Alp, and McAuliffe, Jon D · 2017
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Identification of Gaussian process state space models
Eleftheriadis, Stefanos, Nicholson, Tom, Deisenroth, Marc, and Hensman, James · 2017
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Deep recurrent Gaussian process with variational sparse spectrum approximation
Föll, Roman, Haasdonk, Bernard, Hanselmann, Markus, and Ulmer, Holger · 2017
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DaISy: Database for the identification of systems
Moor, De · 2017
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Hydraulic actuator dataset
Nørgaard, Magnus · 2017
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Doubly stochastic variational inference for deep Gaussian processes
Salimbeni, Hugh and Deisenroth, Marc · 2017
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A flexible state–space model for learning nonlinear dynamical systems
Svensson, Andreas and Schön, Thomas B · 2017
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