  Several studies have shown the ability of natural gradient descent to
minimize the objective function more efficiently than ordinary gradient descent
based methods. However, the bottleneck of this approach for training deep
neural networks lies in the prohibitive cost of solving a large dense linear
system corresponding to the Fisher Information Matrix (FIM) at each iteration.
This has motivated various approximations of either the exact FIM or the
empirical one. The most sophisticated of these is KFAC, which involves a
Kronecker-factored block diagonal approximation of the FIM. With only a slight
additional cost, a few improvements of KFAC from the standpoint of accuracy are
proposed. The common feature of the four novel methods is that they rely on a
direct minimization problem, the solution of which can be computed via the
Kronecker product singular value decomposition technique. Experimental results
on the three standard deep auto-encoder benchmarks showed that they provide
more accurate approximations to the FIM. Furthermore, they outperform KFAC and
state-of-the-art first-order methods in terms of optimization speed.

Neural and Evolutionary Computing;Optimization and Control;Machine Learning