Replication Data for: Exploring Neural Network Weaknesses: Insights from Quantum Principles

The dataset contains the code and raw data for exploiting the accuracy-robustness trade-off from the principle of the uncertainty principle in quantum physics. # The folder contains two sub-folders: "data upload" and "figure&plot". ## In "data upload" the three network structures are used for cifar-10 and mnist. Take the sub-sub-folder "cifar conv" as an example. One starts with the two notebooks named "selected_train_netwrok1_test2.ipynb" and "selected_train_netwrok2_test2.ipynb", where the former performs the training of the complete Convolutional Network while the later divide the convolutional layers into two parts - feature extractor and classifier. After running the the two notebooks, the weights of the networks at each training epoch are saved in the folder "model". Then one runs the other two notebooks named "scanner-x.ipynb" and "scanner-feature-crt.ipynb", where the former performs the Monte-Carlo integrations on multi-GPUs with respect to the normalized loss function of the complete Convolutional Network, while the later only integrates the classifiers (the second part of the complete Convolutional Network). Last, one opens the notebook "plotter.ipynb" to see the results. ## In "figure&plot" we mainly plot the figures in the paper. The txt files are simply copied from the "data upload" folder. To see the figures, one needs to open the file "plot.nb" with Mathematica.