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In this paper, we propose a second order optimization method to learn models where both the dimensionality of the parameter space and the number of training samples is high.
Fast exact multiplication by the Hessian
Barak A. Pearlmutter · 1994
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Natural gradient works efficiently in learning
Shun-Ichi Amari · 1998
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Enriched Methods for Large-Scale Unconstrained Optimization
José Luis Morales and Jorge Nocedal · 2000
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Fast curvature matrix-vector products for second-order gradient descent
Nicol N. Schraudolph · 2002
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Reducing the Dimensionality of Data with Neural Networks
Geoffrey Hinton and Ruslan Salakhutdinov · 2006
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Numerical Optimization
Jorge Nocedal and Stephen J. Wright · 2006
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Topmoumoute online natural gradient algorithm
Nicolas Le Roux, Yoshua Bengio, and Pierre antoine Manzagol · 2007
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Dumitru Erhan, Pierre-Antoine Manzagol, Yoshua Bengio, Samy Bengio, and Pascal Vincent · 2009
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Understanding the difficulty of training deep feedforward neural networks
Yoshua Bengio and Xavier Glorot · 2010
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On the use of stochastic hessian information in optimization methods for machine learning
Richard H. Byrd, Gillian M. Chiny, Will Neveitt, and Jorge Nocedal · 2010
Cited alongside, same era.
Deep learning via Hessian-free optimization
James Martens · 2010
Later among the works it cites.
On optimization methods for deep learning
Quoc V. Le, Jiquan Ngiam, Adam Coates, Abhik Lahiri, Bobby Prochnow, and Andrew Y. Ng · 2011
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Learning Recurrent Neural Networks with Hessian-Free Optimization
James Martens and Ilya Sutskever · 2011
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Comparing Multilayer Perceptron to Deep Belief Network Tandem Features for Robust ASR
Oriol Vinyals and Suman Ravuri · 2011
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