Abstract: A method for selecting samples in a dataset to be labelled in a classification task, wherein the dataset includes N samples belonging to K classes. The method includes: extracting features of the N samples using a feature extractor; statistically inferring a probability distribution of the extracted features in the K classes, to obtain the density of each sample with respect to the K classes distributions; and selecting samples to be labelled using the density.

Abstract: A method for training at least one model able to predict a power consumption or production of at least one electric equipment, also called target.

Abstract: A learning and decoding technique is provided for a neural network. The technique involves using a set of neurons, referred to as beacons, wherein said beacons are binary neurons capable of assuming only two states, an on state and an off state. The beacons are distributed in blocks of a predetermined number of beacons and being allocated for processing a sub-message. Each beacon is associated with a specific occurrence of the sub-message. Learning includes splitting a message into B sub-messages to be learned, where B is greater than or equal to two; activating, for a sub-message, a single beacon in each block to be in the on state, all of the other beacons of the block being in the off state; and activating binary connections between the on beacons of each of the block for a message to be learned, which assume only connected and disconnected states.