Microsoft Technology Licensing, LLC

Richard Eric Turner

Cheng Zhang

Sebastian Tschiatschek

Pashmina Cameron

Yingzhen Li

Camilla Alice Longden

Angus Lamb

Evgeny Sergeevich Saveliev

Jose Miguel Hernández Lobato

Wenbo Gong

José Miguel Hernández Lobato

A computer-implemented method of training an auxiliary machine learning model to predict a set of new parameters of a primary machine learning model, wherein the primary model is configured to transform from an observed subset of a set of real-world features to a predicted version of the set of real-world features.

Auxiliary model for predicting new model parameters

A method of training a model comprising a generative network mapping a latent vector to a feature vector, wherein weights in the generative network are modelled as probabilistic distributions. The method comprises: a) obtaining one or more observed data points, each comprising an incomplete observation of the features in the feature vector; b) training the model based on the observed data points to learn values of the weights of the generative network which map the latent vector to the feature vector; c) from amongst a plurality of potential next features to observe, searching for a target feature of the feature vector which maximizes a measure of expected reduction in uncertainty in a distribution of said weights of the generative network given the observed data points so far; and d) outputting a request to collect a target data point comprising at least the target feature.

Collecting observations for machine learning

Growing community of inventors

Cambridge, United Kingdom

Richard Eric Turner