Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Trusted Neural Networks for Safety-Constrained Autonomous Control   We propose Trusted Neural Network (TNN) models, which are deep neural network
models that satisfy safety constraints critical to the application domain. We
investigate different mechanisms for incorporating rule-based knowledge in the
form of first-order logic constraints into a TNN model, where rules that encode
safety are accompanied by weights indicating their relative importance. This
framework allows the TNN model to learn from knowledge available in form of
data as well as logical rules. We propose multiple approaches for solving this
problem: (a) a multi-headed model structure that allows trade-off between
satisfying logical constraints and fitting training data in a unified training
framework, and (b) creating a constrained optimization problem and solving it
in dual formulation by posing a new constrained loss function and using a
proximal gradient descent algorithm. We demonstrate the efficacy of our TNN
framework through experiments using the open-source TORCS~\cite{BernhardCAA15}
3D simulator for self-driving cars. Experiments using our first approach of a
multi-headed TNN model, on a dataset generated by a customized version of
TORCS, show that (1) adding safety constraints to a neural network model
results in increased performance and safety, and (2) the improvement increases
with increasing importance of the safety constraints. Experiments were also
performed using the second approach of proximal algorithm for constrained
optimization --- they demonstrate how the proposed method ensures that (1) the
overall TNN model satisfies the constraints even when the training data
violates some of the constraints, and (2) the proximal gradient descent
algorithm on the constrained objective converges faster than the unconstrained
version.

20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
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The categories of this paper are: nucl-ex
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The categories of this paper are: econ
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The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin