Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: EA-CG: An Approximate Second-Order Method for Training Fully-Connected
  Neural Networks   For training fully-connected neural networks (FCNNs), we propose a practical
approximate second-order method including: 1) an approximation of the Hessian
matrix and 2) a conjugate gradient (CG) based method. Our proposed approximate
Hessian matrix is memory-efficient and can be applied to any FCNNs where the
activation and criterion functions are twice differentiable. We devise a
CG-based method incorporating one-rank approximation to derive Newton
directions for training FCNNs, which significantly reduces both space and time
complexity. This CG-based method can be employed to solve any linear equation
where the coefficient matrix is Kronecker-factored, symmetric and positive
definite. Empirical studies show the efficacy and efficiency of our proposed
method.

2
The categories of this paper are: math
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys