Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Finite-Sum Optimization: A New Perspective for Convergence to a Global
  Solution   Deep neural networks (DNNs) have shown great success in many machine learning
tasks. Their training is challenging since the loss surface of the network
architecture is generally non-convex, or even non-smooth. How and under what
assumptions is guaranteed convergence to a \textit{global} minimum possible? We
propose a reformulation of the minimization problem allowing for a new
recursive algorithmic framework. By using bounded style assumptions, we prove
convergence to an $\varepsilon$-(global) minimum using
$\mathcal{\tilde{O}}(1/\varepsilon^3)$ gradient computations. Our theoretical
foundation motivates further study, implementation, and optimization of the new
algorithmic framework and further investigation of its non-standard bounded
style assumptions. This new direction broadens our understanding of why and
under what circumstances training of a DNN converges to a global minimum.

11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an