SyReNN: A Tool for Analyzing Deep Neural Networks

• URL: http://arxiv.org/abs/2101.03263v1
• Date: Sat, 9 Jan 2021 00:27:23 GMT
• Title: SyReNN: A Tool for Analyzing Deep Neural Networks
• Authors: Matthew Sotoudeh and Aditya V. Thakur
• Abstract summary: Deep Neural Networks (DNNs) are rapidly gaining popularity in a variety of important domains. This paper introduces SyReNN, a tool for understanding and analyzing a DNN by computing its symbolic representation.
• Score: 8.55884254206878
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep Neural Networks (DNNs) are rapidly gaining popularity in a variety of important domains. Formally, DNNs are complicated vector-valued functions which come in a variety of sizes and applications. Unfortunately, modern DNNs have been shown to be vulnerable to a variety of attacks and buggy behavior. This has motivated recent work in formally analyzing the properties of such DNNs. This paper introduces SyReNN, a tool for understanding and analyzing a DNN by computing its symbolic representation. The key insight is to decompose the DNN into linear functions. Our tool is designed for analyses using low-dimensional subsets of the input space, a unique design point in the space of DNN analysis tools. We describe the tool and the underlying theory, then evaluate its use and performance on three case studies: computing Integrated Gradients, visualizing a DNN's decision boundaries, and patching a DNN.

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