Abstract: A system for adapting a base classifier to one or more novel classes. The base classifier classifies an instance into a base class by extracting a feature representation from the instance using a feature extractor and matching it to class representations of the base classes. The base classifier is adapted using training data for the novel classes. Class representations of the novel classes are determined based on feature representations of instances of the novel classes. The class representations of the novel and base classes are then adapted, wherein at least one class representation of a novel class is adapted based on a class representation of a base class and at least one class representation of a base class is adapted based on a class representation of a novel class. The adapted class representations of the base and novel classes are associated with the base classifier.

Abstract: A computer-implemented method of training a function for use in controlling or monitoring a physical system operating in an environment. The function maps an input instance comprising sensor measurements to an output signal. The function is parameterized by a set of parameters including representations of multiple reference instances. Given a training input instance, a number of reference instances are identified as being similar to the training input instance, and their representations and/or output signals are aggregated into an aggregate latent representation for the training input instance. Based on this aggregate latent representation, an output signal for the training input instance is determined, which is compared to a training output signal to derive a training signal. At least a representation of a reference instance is adjusted according to the training signal, obtaining a reference instance not comprised in the training dataset.

Abstract: A system for adapting a base classifier to one or more novel classes. The base classifier classifies an instance into a base class by extracting a feature representation from the instance using a feature extractor and matching it to class representations of the base classes. The base classifier is adapted using training data for the novel classes. Class representations of the novel classes are determined based on feature representations of instances of the novel classes. The class representations of the novel and base classes are then adapted, wherein at least one class representation of a novel class is adapted based on a class representation of a base class and at least one class representation of a base class is adapted based on a class representation of a novel class. The adapted class representations of the base and novel classes are associated with the base classifier.

Abstract: A machine learning system configured to map an input data instance to an output according to a system mapping. The system mapping may be composed from multiple functions. A latent input vector may be determined in a second latent space for the input data instance, from latent reference vectors related to a number of reference data instances identified as parents.