Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Neural Networks under Input-Output Specifications   In this paper, we examine an important problem of learning neural networks
that certifiably meet certain specifications on input-output behaviors. Our
strategy is to find an inner approximation of the set of admissible policy
parameters, which is convex in a transformed space. To this end, we address the
key technical challenge of convexifying the verification condition for neural
networks, which is derived by abstracting the nonlinear specifications and
activation functions with quadratic constraints. In particular, we propose a
reparametrization scheme of the original neural network based on loop
transformation, which leads to a convex condition that can be enforced during
learning. This theoretical construction is validated in an experiment that
specifies reachable sets for different regions of inputs.

31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics