Revisiting Natural Gradient for Deep Networks
We evaluate natural gradient, an algorithm originally proposed in Amari (1997), for learning deep models. The contributions of this paper are as follows. We show the connection between natural gradient and three other recently proposed methods for training deep models: Hessian-Free (Martens, 2010), Krylov Subspace Descent (Vinyals and Povey, 2012) and TONGA (Le Roux et al., 2008). We describe how
The Emerging Information Geometric Approach to Deep Learning | Alluviate Blog
Classical Statistics addresses systems involving large numbers, however Statistics breaks down in a domain of high dimensional inputs and models with a high number of parameters. In this domain, new theories and methods are being developed using new insights discovered though the use of massive computational systems. The field of Deep Learning is spearheading these discoveries, however there is a
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