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Kalman Filter requires the true parameters of the model and solves optimal state estimation recursively.
A new approach to linear filtering and prediction problems
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Machine learning, a probabilistic perspective
Kevin Murphy · 2012
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Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Xu Bing, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le · 2014
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Adam: a method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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An em algorithm for nonlinear state estimation with model uncertainties
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