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Found 3 papers, 2 papers with code
Harnessing Neuron Stability to Improve DNN Verification
We evaluate the effectiveness of VeriStable across a range of challenging benchmarks including fully-connected feedforward networks (FNNs), convolutional neural networks (CNNs) and residual networks (ResNets) applied to the standard MNIST and CIFAR datasets.
Inferring Properties of Graph Neural Networks
To tackle the challenge of varying input structures in GNNs, GNNInfer first identifies a set of representative influential structures that contribute significantly towards the prediction of a GNN.
A DPLL(T) Framework for Verifying Deep Neural Networks
Deep Neural Networks (DNNs) have emerged as an effective approach to tackling real-world problems.
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