Abstract: A computerized method of estimating resource requirements in an environment is presented. The method comprises a preparation phase and a simulation phase, wherein the preparation phase comprises a machine learning training phase and a clustering phase. The machine learning training phase trains a machine learning model to predict a resource requirement. Thereby, a subset of features is extracted. The clustering phase determines clusters in the subset of features, a correlation coefficient and least one identifying parameter of a distribution of the feature values of the subset of features. Finally, the simulation phase determines a distribution for a feature, selects at least one value for a feature and uses the second machine learning model to estimate a resource requirement in the environment in at least one time period the future.

Abstract: A computing device, that is configured to configure a global machine learning model, performs respective electronic risk audits of client devices configured to train respective local machine learning models that correspond to a global machine learning model. Based on respective electronic risk scores of one or more of the client devices, determined via the respective electronic risk audits, the computing device implements one or more parameter privacy adjustment methods on respective parameters received from the client devices prior to using the respective parameters to configure the global machine learning model, wherein respective client devices determined to have higher electronic risk scores have more of the parameter privacy adjustment methods applied than other respective client devices determined to have lower electronic risk scores. The computing device provides, to the client devices, the global machine learning model configured according to the respective parameters as adjusted.

Abstract: A computing device generates a neural network (NN) comprising an architecture of a plurality of levels of respective groups of respective neurons, a last level of the NN split into groups corresponding to a respective number of a given set of NN classes, at least one child group of a given level of the NN being disconnected from other groups of the given level. The computing device associates the groups of the last level with a respective subset of the given set of NN classes. The computing device trains the NN using a training dataset, the training dataset comprising inputs and outputs corresponding to the given set of the NN classes.

Abstract: A method of generating a training image dataset from an input image of an imaged object comprises: generating a three-dimensional model of the imaged object from the input image; and generating synthesized two-dimensional images representing three-dimensional model, by simulating a plurality of image captures of the model. For each of the simulated image captures, a theoretical value for a capture parameter at least partially characterising the simulated image capture is set to a respective one of a plurality of different values. The input image is used as a reference image. For each synthesized image, a distortion amount in the synthesized image compared with the reference image is calculated, and a calibrated capture parameter value for each synthesized image is determined, based on at least the calculated distortion amount of the synthesized image, distortion amounts calculated from real acquired images, and capture parameter values used to acquire the real acquired images.

Abstract: The present specification provides a system and method to process images including biometric data. The method includes use of synthetic images, without use of personal identifiable information (PII). The synthetic images can be used for different applications such as to provide a machine learning dataset that can be used to control output devices.

Abstract: A computerized method of estimating resource requirements in an environment is presented. The method comprises a preparation phase and a simulation phase, wherein the preparation phase comprises a machine learning training phase and a clustering phase. The machine learning training phase trains a machine learning model to predict a resource requirement. Thereby, a subset of features is extracted. The clustering phase determines clusters in the subset of features, a correlation coefficient and least one identifying parameter of a distribution of the feature values of the subset of features. Finally, the simulation phase determines a distribution for a feature, selects at least one value for a feature and uses the second machine learning model to estimate a resource requirement in the environment in at least one time period the future.

Abstract: Methods, systems, and computer program products for determining user representations based on matching. A matching request associated with a user is received. Event search data for a plurality of events for the plurality of users is obtained. A merged user representation for a plurality of candidates associated with the plurality of users is generated based on the event search data. A subset of candidates from the plurality of candidates is selected based on the merged user representation. Pairwise features are determined based on similarities between the subset of the candidates. A learned user representation is determined by identifying, using a machine learning algorithm, at least one user of the plurality of users from the subset of the candidates based on the pairwise features. The learned user representation associated with the at least one identified user of the plurality of users is provided.

Abstract: Computer-implemented methods of providing personalized recommendations to a user of items available in an online system, and related systems. First-level features including context features are computed based upon context data. A first-level machine learning model is then evaluated using the first-level features to generate predictions of user behavior in relation to a plurality of individual items available via the online system. A list of proposed item recommendations is constructed based upon the predictions. Second-level features are computed based upon the context data and list features based upon the list of proposed item recommendations and the corresponding predictions generated by the first-level machine learning model. A second-level machine learning model is evaluated using the second-level features to generate a prediction of user behavior in relation to the list of proposed item recommendations.

Abstract: Computer-implemented methods of providing personalized recommendations to a user of items available in an online system, and related systems. First-level features including context features are computed based upon context data. A first-level machine learning model is then evaluated using the first-level features to generate predictions of user behavior in relation to a plurality of individual items available via the online system. A list of proposed item recommendations is constructed based upon the predictions. Second-level features are computed based upon the context data and list features based upon the list of proposed item recommendations and the corresponding predictions generated by the first-level machine learning model. A second-level machine learning model is evaluated using the second-level features to generate a prediction of user behavior in relation to the list of proposed item recommendations.