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Bayesian filtering approximates the true underlying behavior of a time-varying system by inverting an explicit generative model to convert noisy measurements into state estimates.
A note on the delta-method for finding variance formulae
R.A. Dorfman · 1938
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A new approach to linear filtering and prediction problems
Rudolph Emil Kalman · 1960
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Deterministic nonperiodic flow
Edward N. Lorenz · 1963
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Approaches to adaptive filtering
Raman Mehra · 1972
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Applied optimal estimation
Arthur Gelb · 1974
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A new spline algorithm for non-linear filtering of discrete time systems
M.L. Andrade Netto, L. Gimeno, and M.J. Mendes · 1978
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Training multilayer perceptrons with the extended Kalman algorithm
Sharad Singhal and Lance Wu · 1988
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Comparative analysis of backpropagation and the extended Kalman filter for training multilayer perceptrons
Dennis W. Ruck, Steven K. Rogers, Matthew Kabrisky, Peter S. Maybeck, and Mark E. Oxley · 1992
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The iterated Kalman filter update as a Gauss-Newton method
Bradley M. Bell and Frederick W. Cathey · 1993
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Novel approach to nonlinear/non-Gaussian Bayesian state estimation
Neil J. Gordon, David J. Salmond, and Adrian F.M. Smith · 1993
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A new approach for filtering nonlinear systems
Simon J. Julier, Jeffrey K. Uhlmann, and Hugh F. Durrant-Whyte · 1995
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Incremental least squares methods and the extended Kalman filter
Dimitri P. Bertsekas · 1996
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Monte Carlo filter and smoother for non-Gaussian nonlinear state space models
Genshiro Kitagawa · 1996
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Equivalence of regularization and truncated iteration for general ill-posed problems
Reginaldo J. Santos · 1996
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Hierarchical Bayesian models for regularization in sequential learning
Nando De Freitas, Mahesan Niranjan, and Andrew H. Gee · 2000
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Sequential Monte Carlo methods in practice
Arnaud Doucet, Nando De Freitas, and Neil James Gordon · 2001
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Bayesian filtering: From Kalman filters to particle filters, and beyond
Zhe Chen · 2003
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Kalman filtering and neural networks
Simon Haykin · 2004
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Unscented filtering and nonlinear estimation
Simon J. Julier and Jeffrey K. Uhlmann · 2004
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Optimal state estimation: Kalman, H infinity, and nonlinear approaches
Dan Simon · 2006
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Data assimilation: the ensemble Kalman filter
Geir Evensen · 2009
Cited alongside, same era.
The variational Kalman filter and an efficient implementation using limited memory BFGS
Harri Auvinen, Johnathan M. Bardsley, Heikki Haario, and T. Kauranne · 2010
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Early stopping as nonparametric variational inference
David Duvenaud, Dougal Maclaurin, and Ryan Adams · 2016
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Deep learning
Yoshua Bengio, Ian Goodfellow, and Aaron Courville · 2017
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A novel adaptive Kalman filter with inaccurate process and measurement noise covariance matrices
Yulong Huang, Yonggang Zhang, Zhemin Wu, Ning Li, and Jonathon Chambers · 2017
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Weak in the NEES?: Auto-tuning Kalman filters with Bayesian optimization
Zhaozhong Chen, Christoffer Heckman, Simon Julier, and Nisar Ahmed · 2018
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Online natural gradient as a Kalman filter
Yann Ollivier · 2018
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
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A fresh look at the Kalman filter
Jeffrey Humpherys, Preston Redd, and Jeremy West · 2012
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Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tijmen Tieleman and Geoffrey Hinton · 2012
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Who Invented the Delta Method?
Jay M. Ver Hoef · 2012
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Adadelta: an adaptive learning rate method
Matthew D. Zeiler · 2012
Cited alongside, same era.
The extended Kalman filter is a natural gradient descent in trajectory space
Yann Ollivier · 2019
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On computational complexity reduction methods for Kalman filter extensions
Matti Raitoharju and Robert Piché · 2019
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Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods
Laurence Aitchison · 2020
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Scalable gradients for stochastic differential equations
Xuechen Li, Ting-Kam Leonard Wong, Ricky T.Q. Chen, and David Duvenaud · 2020
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New insights and perspectives on the natural gradient method
James Martens · 2020
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Non-linear Gaussian smoothing with Taylor moment expansion
Zheng Zhao and Simo Särkkä · 2021
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Low-rank extended Kalman filtering for online learning of neural networks from streaming data
Peter Chang, Gerardo Duràn-Martín, Alexander Y Shestopaloff, Matt Jones, and Kevin Murphy · 2023
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The Bayesian learning rule
Mohammad Emtiyaz Khan and Håvard Rue · 2023
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Bayesian filtering and smoothing , volume 17
Simo Särkkä and Lennart Svensson · 2023
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