In this work, we present an early prototype of NeVer 2.0, a new system for automated synthesis and analysis of deep neural networks.NeVer 2.0borrows its design philosophy from NeVer, the first package that integrated learning, automated verification and repair of (shallow) neural networks in a single tool. The goal of NeVer 2.0 is to provide a similar integration for deep networks by leveraging a selection of state-of-the-art learning frameworks and integrating them with verification algorithms to ease the scalability challenge and make repair of faulty networks possible.
@article{arxiv.2011.09933,
title = {NeVer 2.0: Learning, Verification and Repair of Deep Neural Networks},
author = {Dario Guidotti and Luca Pulina and Armando Tacchella},
journal= {arXiv preprint arXiv:2011.09933},
year = {2020}
}
Comments
arXiv admin note: text overlap with arXiv:2003.07636