Browsing Faculty of Engineering by Subject "Deep Learning"

Now showing items 1-4 of 4

    • Benign interpolation of noise in deep learning 

      Davel, Marelie Hattingh; Barnard, Etienne; Theunissen, Marthinus Wilhelmus (South African Institute of Computer Scientists and Information Technologists, 2020)
      The understanding of generalisation in machine learning is in a state of flux, in part due to the ability of deep learning models to interpolate noisy training data and still perform appropriately on out-of-sample data, ...

    • Pre-interpolation loss behavior in neural networks 

      Venter, Arthur Edgar William; Theunissen, Marthinus Wilhelm; Davel, Marelie Hattingh (Springer, 2020)
      When training neural networks as classifiers, it is common to observe an increase in average test loss while still maintaining or improving the overall classification accuracy on the same dataset. In spite of the ubiquity ...

    • Tracking translation invariance in CNNs 

      Myburgh, Johannes C.; Mouton, Coenraad; Davel, Marelie H. (Southern African Conference for Artificial Intelligence Research, 2020)
      Although Convolutional Neural Networks (CNNs) are widely used, their translation invariance (ability to deal with translated inputs) is still subject to some controversy. We explore this question using translation-sensitivity ...

    • Using summary layers to probe neural network behaviour 

      Davel, Marelie Hattingh (South African Institute of Computer Scientists and Information Technologists, 2020)
      No framework exists that can explain and predict the generalisation ability of deep neural networks in general circumstances. In fact, this question has not been answered for some of the least complicated of neural network ...