Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Qualitatively characterizing neural network optimization problems   Training neural networks involves solving large-scale non-convex optimization
problems. This task has long been believed to be extremely difficult, with fear
of local minima and other obstacles motivating a variety of schemes to improve
optimization, such as unsupervised pretraining. However, modern neural networks
are able to achieve negligible training error on complex tasks, using only
direct training with stochastic gradient descent. We introduce a simple
analysis technique to look for evidence that such networks are overcoming local
optima. We find that, in fact, on a straight path from initialization to
solution, a variety of state of the art neural networks never encounter any
significant obstacles.

32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
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The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin