Add to ORKGThe increasing use of deep neural networks in a variety of applications, including some safety-critical ones, has brought renewed interest in the topic of verification of neural networks. However, verification is most meaningful when performed with high-quality formal specifications. In this paper, we survey the landscape of formal specification for deep neural networks, and discuss the opportunities and challenges for formal methods for this domain
The increasing use of deep neural networks for safety-critical applications, such as autonomous driv...
We introduce a model for agent-environment systems where the agents are implemented via feed-forward...
The success of Deep Learning and its potential use in many safety-critical applications has motivate...
The increasing use of deep neural networks in a variety of applications, including some safety-criti...
Machine learning models and in particular Deep Neural Networks are being deployed in an ever increas...
The success of Deep Learning and its potential use in many safety-critical applications has motivate...
In recent years, a number of methods for verifying DNNs have been developed. Because the approaches ...
Deep neural networks (DNNs) have found diverse applications such as image processing, video process...
Computing systems are becoming ever more complex, increasingly often incorporating deep learning com...
This paper introduces a formal framework for describing and specifying neural networks and discusses...
Understanding properties of deep neural networks is an important challenge in deep learning. Deep le...
Neural Networks (NNs) are popular machine learning models which have found successful application in...
We present a novel method for scalable and precise certification of deep neural networks. The key te...
Complete verification of deep neural networks (DNNs) can exactly determine whether the DNN satisfies...
In order to assist the field of neural networks in maturing, a formalization and a solid foundation ...
The increasing use of deep neural networks for safety-critical applications, such as autonomous driv...
We introduce a model for agent-environment systems where the agents are implemented via feed-forward...
The success of Deep Learning and its potential use in many safety-critical applications has motivate...
The increasing use of deep neural networks in a variety of applications, including some safety-criti...
Machine learning models and in particular Deep Neural Networks are being deployed in an ever increas...
The success of Deep Learning and its potential use in many safety-critical applications has motivate...
In recent years, a number of methods for verifying DNNs have been developed. Because the approaches ...
Deep neural networks (DNNs) have found diverse applications such as image processing, video process...
Computing systems are becoming ever more complex, increasingly often incorporating deep learning com...
This paper introduces a formal framework for describing and specifying neural networks and discusses...
Understanding properties of deep neural networks is an important challenge in deep learning. Deep le...
Neural Networks (NNs) are popular machine learning models which have found successful application in...
We present a novel method for scalable and precise certification of deep neural networks. The key te...
Complete verification of deep neural networks (DNNs) can exactly determine whether the DNN satisfies...
In order to assist the field of neural networks in maturing, a formalization and a solid foundation ...
The increasing use of deep neural networks for safety-critical applications, such as autonomous driv...
We introduce a model for agent-environment systems where the agents are implemented via feed-forward...
The success of Deep Learning and its potential use in many safety-critical applications has motivate...