Abstract: A computer-implemented method of predicting energy use for a route including inputting map data of roads included in K trips in a geographical area, predictors of rate of energy use along the roads, and energy consumption data of the K trips. The method includes dividing each of the roads in the map data for all the trips into segments of length measure ?i; grouping the segments from the trips into a number N of clusters, using an algorithm to build a model predicting the weights Wj based on solving a system of equations, one per trip, assigning the predicted weight applied to the cluster in which the segment was grouped and storing a segment ID with the corresponding cluster ID or predicted rate of energy use Yi to allow prediction of energy use for a route in the geographical area incorporating one or more of the segments.

Abstract: A computer-implemented method comprising: obtaining, based on an input image, a first activation map of a labelled filter of a first convolutional neural network, the first convolutional neural network being configured to identify one or more first features in the input image; obtaining, based on the input image, a second activation map of a filter of a second convolutional neural network, the second convolutional neural network being configured to identify one or more second features in the input image; calculating a similarity measure between the first activation map and the second activation map; and labelling, when the similarity measure is equal to or above a threshold similarity, the filter of the second convolutional neural network with a label of the labelled filter of the first convolutional neural network.

Abstract: Rules for explaining the output of an ANN are derived by: creating decision trees trained to approximate the ANN and optimize a defined criterion, a threshold value for the criterion being calculated to determine for which node of the ANN the input activations should be split between branches of the decision tree; obtaining threshold value combinations each comprising a threshold value obtained for respective nodes of the ANN; for each combination, using the combination to perform a rule extraction algorithm to extract a rule explaining the output of the ANN and to obtain a fidelity metric indicating the accuracy of the rule with respect to predictions of the ANN; determining which combination yields the best fidelity metric; and using the rule extraction algorithm with the combination of threshold values determined to yield the best fidelity metric to extract at least one rule for explaining the output of the ANN.

Abstract: A computer-implemented method of training a deep neural network to classify data comprises: for a batch of N training data Xi, where i=1 to N and ci is the class of training data Xi, carrying out a clustering-based regularization process at at least one layer l of the DNN having neurons j, in which process a regularization activity penalty is added to a loss function for the batch of training data which is to be optimized during training, whereby the regularization activity penalty comprises components associated with respective neurons in the layer which are dependent on the respective classes of the training data.

Abstract: A computer-implemented method of predicting energy use for a route including inputting map data of roads included in K trips in a geographical area, predictors of rate of energy use along the roads, and energy consumption data of the K trips. The method includes dividing each of the roads in the map data for all the trips into segments of length measure ?i; grouping the segments from the trips into a number N of clusters, using an algorithm to build a model predicting the weights Wj based on solving a system of equations, one per trip, assigning the predicted weight applied to the cluster in which the segment was to grouped and storing a segment ID with the corresponding cluster ID or predicted rate of energy use Yi to allow prediction of energy use for a route in the geographical area incorporating one or more of the segments.