Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Identifying Generalization Properties in Neural Networks   While it has not yet been proven, empirical evidence suggests that model
generalization is related to local properties of the optima which can be
described via the Hessian. We connect model generalization with the local
property of a solution under the PAC-Bayes paradigm. In particular, we prove
that model generalization ability is related to the Hessian, the higher-order
"smoothness" terms characterized by the Lipschitz constant of the Hessian, and
the scales of the parameters. Guided by the proof, we propose a metric to score
the generalization capability of the model, as well as an algorithm that
optimizes the perturbed model accordingly.

23
The categories of this paper are: solv-int
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
30
The categories of this paper are: q-alg