Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Geometry and Generalization: Eigenvalues as predictors of where a
  network will fail to generalize   We study the deformation of the input space by a trained autoencoder via the
Jacobians of the trained weight matrices. In doing so, we prove bounds for the
mean squared errors for points in the input space, under assumptions regarding
the orthogonality of the eigenvectors. We also show that the trace and the
product of the eigenvalues of the Jacobian matrices is a good predictor of the
MSE on test points. This is a dataset independent means of testing an
autoencoder's ability to generalize on new input. Namely, no knowledge of the
dataset on which the network was trained is needed, only the parameters of the
trained model.

18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess