1. Re:Verification of Neural Networks_阅读报告 这篇文章的第五部分提出删除对结果不重要的神经元和把批归一化层编码为线性层,删除部分神经元的操作我认为有点类似于dropout,但是为什么有些验证工具不能接受具有批归一化层的网络呢?我看训练神经网络的时... --第2小组赖妍菱 2. Re:Verification of Neural Netw...
Fairness is crucial for neural networks which are used in applications with important societal implication. Recently, there have been multiple attempts on improving fairness of neural networks, with a focus on fairness testing (e.g., generating individual discriminatory instances) and fairness training ...
We provide an overview of algorithmic approaches that aim to provide formal guarantees on the behaviour of neural networks. Moreover, we present new theoretical results with respect to the approximation of ReLU neural networks. On the other hand, we implement a solver for verification of ReLU ...
Deep neural networks have achieved impressive experimental results in image classification, but can surprisingly be unstable with respect to adversarial perturbations, that is, minimal changes to the input image that cause the network to misclassify it. With potential applications including perception module...
In this paper, we investigate how satisfiability modulo theory (SMT) technologies can enable the verification of neural networks that exhibit both piece-wise linear and transcendental activation functions. The inherent non-linearity of these functions presents significant scalability challenges, and our goa...
A classification network N can be used as a decision-making algorithm: given an input α, it suggests a decision N(α) among a set of possible decisions. While the accuracy of neural networks has greatly improved, matching the cognitive ability of humans [6], they are susceptible to ...
1. Neural networks and correctness 2. Neural networks as graphs 3. Correctness properties Part II Constraint-based verification 4. Logic and satisfiability 5. Encodings of neural networks 6. DPLL modulo theories 7. Neural theory solvers Part III Abstraction-based verification ...
This paper addresses the problem of formally verifying desirable properties of neural networks, i.e., obtaining provable guarantees that neural networks satisfy specifications relating their inputs and outputs (robustness to bounded norm adversarial perturbations, for example). Most previous work on this...
Pacut A.: Neural networks for signature classification and identity verificationA digitizing tablet was employed to collect handwritten signatures with five ... A Czajka,A Pacut 被引量: 0发表: 2008年 Fusion of face and speech data for person identity verification Biometric person identity authentica...
Handwritten signatures are considered as the mostnatural method of authenticating a person's identity (comparedto other biometric and cryptographic forms of authentication).The learning process inherent in Neural Networks (NN) can beapplied to the process of verifying handwritten signatures thatare ...