Titlebook: Computer Aided Verification; 32nd International C Shuvendu K. Lahiri,Chao Wang Conference proceedings‘‘‘‘‘‘‘‘ 2020 The Editor(s) (if applic

Gum-Disease

Quality Improvement Techniques,tributed one where the cost of imposing a (logical) global ordering on all actions is prohibitive. In this paper, we tackle these issues by proposing appropriate semantics and specifications for highly-concurrent libraries in a weakly-consistent, replicated setting. We use these specifications to de

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Improved Geometric Path Enumeration for Verifying ReLU Neural Networksmission-critical and safety-critical applications, however, it is important to be able to analyze what a neural network can and cannot do. For feed-forward neural networks with ReLU activation functions, although exact analysis is NP-complete, recently-proposed verification methods can sometimes suc

Commentary

Systematic Generation of Diverse Benchmarks for DNN Verificationthis by supporting the assessment of the state-of-the-art and comparison of alternative verification approaches. Recent years have witnessed significant developments in the verification of deep neural networks, but diverse benchmarks representing the range of verification problems in this domain do

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Formal Analysis and Redesign of a Neural Network-Based Aircraft Taxiing System with . . provides an integrated toolchain for tasks spanning the design process, including modeling, falsification, debugging, and ML component retraining. We evaluate all of these applications in an industrial case study on an experimental autonomous aircraft taxiing system developed by Boeing, which us

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