Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Analyzing Training Using Phase Transitions in Entropy---Part I: General
  Theory   We analyze phase transitions in the conditional entropy of a sequence caused
by a change in the conditional variables. Such transitions happen, for example,
when training to learn the parameters of a system, since the transition from
the training phase to the data phase causes a discontinuous jump in the
conditional entropy of the measured system response. For large-scale systems,
we present a method of computing a bound on the mutual information obtained
with one-shot training, and show that this bound can be calculated using the
difference between two derivatives of a conditional entropy. The system model
does not require Gaussianity or linearity in the parameters, and does not
require worst-case noise approximations or explicit estimation of any unknown
parameters. The model applies to a broad range of algorithms and methods in
communication, signal processing, and machine learning that employ training as
part of their operation.

13
The categories of this paper are: quant-ph
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org