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\title{VEIN: VErification via Interaction Nets for Neural Networks}
\author{Eric Marin}
\date{\today}

\begin{document}

\maketitle

\begin{abstract}
	This thesis introduces \textbf{VEIN} (VErification via Interaction Nets), a framework for neural network
	verification with a focus on neural network equivalence. It acts as a	formally verified
	preprocessor that reduces neural networks to a normal form before they can be compared by a solver.
	To enable this reduction, \textbf{VEIN} translates neural networks into Interaction Nets, a graph rewriting
	computational model, and applies a set of graph rewriting rules to reduce the network into an
	Abstract Syntax Tree. We present the complete framework and provide in detail: the translation
	process, the graph rewriting rules and rigorous proofs of both soundness and termination for the
	reduction process.
\end{abstract}

\tableofcontents

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% Related Work
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