论文标题
从不2.0：深度神经网络的学习，验证和修复
NeVer 2.0: Learning, Verification and Repair of Deep Neural Networks
论文作者
论文摘要
在这项工作中，我们介绍了Never 2.0的早期原型，这是一种用于自动合成和深层神经网络分析的新系统。从未将其设计理念从Never中获得。 Never 2.0的目标是通过利用最先进的学习框架并将其集成到验证算法来减轻可扩展性挑战并使错误网络的维修成为可能，从而为深网提供类似的集成。
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.
