Marabou 2.0: A Versatile Formal Analyzer of Neural Networks | SpringerLink Skip to main
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Researchers have developed neural network verification algorithms motivated by the need
to characterize the robustness of deep neural networks. The verifiers aspire to answer …

pyNeVer: A Framework for Learning and Verification of Neural Networks | SpringerLink Skip
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The availability of representative datasets is an essential prerequisite for many successful
artificial intelligence and machine learning models. However, in real life applications these …

Interest in machine learning and neural networks has increased significantly in recent years.
However, their applications are limited in safety-critical domains due to the lack of formal …

Planning for real-world applications requires algorithms and tools with the ability to handle
the complexity such scenarios entail. However, meeting the needs of such applications …

摘要在大数据时代下, 深度学**理论和技术取得的突破性进展, 为人工智能提供了数据和算法
层面的**有力支撑, 同时促进了深度学**的规模化和产业化发展. 然而, 尽管深度学**模型在现实 …

In recent years, there has been growing interest in machine learning and neural networks
within research and industrial communities. While neural networks have shown impressive …

In the last couple of decades, the popularity of neural networks has soared and they have
been successfully utilized in many different domains across computer science. However …

It has increasingly been recognised that verification can contribute to the validation and
debugging of neural networks before deployment, particularly in safety-critical areas. While …