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In this work, we present a new derivative-free optimization method and investigate its use for training neural networks.
Learning representations by back-propagating errors
D.E. Rumelhart, Geoffrey Hinton, and J. Williams, R · 1986
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Numerical aspects of different kalman filter implementations
Micheal Verhaegen and Paul Van Dooren · 1986
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Handwritten digit recognition with a back-propagation network
Y LeCun, B E Boser, and J S Denker · 1990
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An ensemble Kalman smoother for nonlinear dynamics
G. Evensen and P. J. van Leeuwen · 2000
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P. L. Houtekamer and H. L. Mitchell · 2001
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The Ensemble Kalman Filter: theoretical formulation and practical implementation
Geir Evensen · 2003
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A local ensemble kalman filter for atmospheric data assimilation
Edward Ott, Brian R Hunt, Istvan Szunyogh, Aleksey V Zimin, Eric J Kostelich, Matteo Corazza, Eugenia Kalnay, DJ Patil, and James A Yorke · 2004
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The empirical behavior of sampling methods for stochastic programming
J.J. Linderoth, A. Shapiro, and S. Wright · 2006
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Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter
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Stochastic approximation approach to stochastic programming
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Lectures on Stochastic Programming: Modeling and Theory
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4D Seismic History Matching Using the Ensemble Kalman Filter (EnKF): Possibilities and Challenges
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Incremental gradient, subgradient, and proximal methods for convex optimization: A survey
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Implicit Filtering
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Application Of An Extended Kalman Filter Approach To Inversion Of Time-lapse Electrical Resistivity Imaging Data For Monitoring Infiltration
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Ensemble kalman methods for inverse problems
Marco A Iglesias, Kody J H Law, and Andrew M Stuart · 2013
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Iterative regularization for ensemble data assimilation in reservoir models
Marco A. Iglesias · 2015
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Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang · 2016
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Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning
F. Petroski Such, V. Madhavan, E. Conti, J. Lehman, K. O. Stanley, and J. Clune · 2017
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Analysis of the ensemble kalman filter for inverse problems
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L Bottou · 2012
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A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets
N. Le Roux, M. Schmidt, and F. Bach · 2012
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Deep learning via semi-supervised embedding
J. Weston, F. Ratle, H. Mobahi, and R. Collobert · 2012
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C. Schillings and A. M. Stuart · 2017
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Theory of deep learning iii: Generalization properties of sgd
Chiyuan Zhang, Qianli Liao, Alexander Rakhlin, Karthik Sridharan, Brando Miranda, Noah Golowich, and Tomaso Poggio · 2017
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Derivative-free ensemble methods for machine learning tasks (slides for presentation at CM+X Workshop available at http://cmx.caltech.edu/ipml-program.html), February 2018
Nikola Kovachki and Andrew M Stuart · 2018
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Ensemble Kalman inversion: a derivative-free technique for machine learning tasks
Nikola Kovachki and Andrew M Stuart · 2018
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