Abstract: One embodiment provides a computer implemented method, including: receiving information corresponding to a customer of a seller, wherein the information is related to credit information of the customer; generating a credit attribute for the customer with respect to the seller, wherein the generating includes utilizing a plurality of artificial intelligence agents that each analyze at least a subset of the information to each generate an agent version of the credit attribute; and recommending a deferral of at least a portion of a pending invoice of the seller for the customer, wherein a value of the deferral is based upon the credit attribute.

Abstract: Methods, systems, and computer program products for determining operating range of hyperparameters are provided herein. A computer-implemented method includes obtaining a machine learning model, a list of candidate values for a hyperparameter, and a dataset; performing one or more hyperparameter range trials based on the machine learning model, the list of candidate values for the hyperparameter, and the dataset; automatically determining an operating range of the hyperparameter based on the one or more hyperparameter range trials; and training the machine learning model to convergence based at least in part on the determined operating range.

Abstract: One embodiment provides a computer implemented method, including: receiving information corresponding to a customer of a seller, wherein the information is related to credit information of the customer; generating a credit attribute for the customer with respect to the seller, wherein the generating includes utilizing a plurality of artificial intelligence agents that each analyze at least a subset of the information to each generate an agent version of the credit attribute; and recommending a deferral of at least a portion of a pending invoice of the seller for the customer, wherein a value of the deferral is based upon the credit attribute.

Abstract: One embodiment provides a computer implemented method, including: receiving billing information related to a billing contract of a customer of a seller, wherein the billing contract identifies amounts of invoices and an invoice frequency; identifying, utilizing one or more artificial intelligence agents, one or more risk factors associated with generation of a pending invoice based upon the billing information; and recommending, utilizing the one or more artificial intelligence agents, a generation date for the pending invoice based upon the one or more risk factors, wherein the recommending includes selecting a generation date to facilitate timely payment of the pending invoice by the customer.

Abstract: Methods, systems, and computer program products for determining operating range of hyperparameters are provided herein. A computer-implemented method includes obtaining a machine learning model, a list of candidate values for a hyperparameter, and a dataset; performing one or more hyperparameter range trials based on the machine learning model, the list of candidate values for the hyperparameter, and the dataset; automatically determining an operating range of the hyperparameter based on the one or more hyperparameter range trials; and training the machine learning model to convergence based at least in part on the determined operating range.

Abstract: Methods, systems, and computer program products for generating capacity planning schedules while protecting the privacy of stakeholder preferences of a set of metrics are provided herein. A computer-implemented method includes identifying stakeholders associated with capacity planning for a project; determining metrics to be used in the capacity planning; obtaining, from each of the stakeholders, an initial preferred order of emphasis of the metrics; calculating similarity scores between the initial preferred orders of emphasis; outputting, to each of the stakeholders, the similarity scores, wherein the identity of the stakeholders has been masked; obtaining, from each of the stakeholders, at least a second iteration of a preferred order of emphasis of the metrics; generating a final order of emphasis of the multiple metrics upon a determination that the stakeholders provided at least a predetermined number of identical preferred orders of emphasis; and outputting the final order of emphasis of the metrics.

Abstract: Embodiments for recommending exemplars of a data-set by a processor. A selected number of exemplars may be labeled from one or more classes in a data-set. One or more class exemplars for each of the one or more classes in the data-set may be recommended according to similarities between the selected number of labeled exemplars and remaining data of the data-set.

Abstract: Methods, systems, and computer program products for generating capacity planning schedules while protecting the privacy of stakeholder preferences of a set of metrics are provided herein. A computer-implemented method includes identifying stakeholders associated with capacity planning for a project; determining metrics to be used in the capacity planning; obtaining, from each of the stakeholders, an initial preferred order of emphasis of the metrics; calculating similarity scores between the initial preferred orders of emphasis; outputting, to each of the stakeholders, the similarity scores, wherein the identity of the stakeholders has been masked; obtaining, from each of the stakeholders, at least a second iteration of a preferred order of emphasis of the metrics; generating a final order of emphasis of the multiple metrics upon a determination that the stakeholders provided at least a predetermined number of identical preferred orders of emphasis; and outputting the final order of emphasis of the metrics.

Abstract: Embodiments for recommending exemplars of a data-set by a processor. A selected number of exemplars may be labeled from one or more classes in a data-set. One or more class exemplars for each of the one or more classes in the data-set may be recommended according to similarities between the selected number of labeled exemplars and remaining data of the data-set.

Abstract: Embodiments for estimating substitutability between skills by combining skill similarities from one or more data sources by a processor. An adjacency of skill similarity of one or more skills of one or more entities may be determined. The adjacency of skill similarity may be used to generate one or more skill clusters. Skill demand of the one or more skill clusters may be forecasted.

Abstract: A method of computerized data analysis and synthesis is described. First and second datasets of a quantity of interest are stored. A Gaussian process model is generated using the first and second datasets to compute optimized kernel and noise hyperparameters. The Gaussian process model is applied using the stored first and second datasets and hyperparameters to perform Gaussian process regression to compute estimates of unknown values of the quantity of interest. The resulting computed estimates of the quantity of interest result from a non-parametric Gaussian process fusion of the first and second measurement datasets. The first and second datasets may be derived from the same or different measurement sensors. Different sensors may have different noise and/or other characteristics.

Abstract: A method for modelling a dataset includes a training phase, wherein the dataset is applied to a non-stationary Gaussian process kernel in order to optimize the values of a set of hyperparameters associated with the Gaussian process kernel, and an evaluation phase in which the dataset and Gaussian process kernel with optimized hyperparameters are used to generate model data. The evaluation phase includes a nearest neighbor selection step. The method may include generating a model at a selected resolution.

Abstract: A method of computerised data analysis and synthesis is described. First and second datasets of a quantity of interest are stored. A Gaussian process model is generated using the first and second datasets to compute optimized kernel and noise hyperparameters. The Gaussian process model is applied using the stored first and second datasets and hyperparameters to perform Gaussian process regression to compute estimates of unknown values of the quantity of interest. The resulting computed estimates of the quantity of interest result from a non-parametric Gaussian process fusion of the first and second measurement datasets. The first and second datasets may be derived from the same or different measurement sensors. Different sensors may have different noise and/or other characteristics.

Abstract: A method for modelling a dataset includes a training phase, wherein the dataset is applied to a non-stationary Gaussian process kernel in order to optimize the values of a set of hyperparameters associated with the Gaussian process kernel, and an evaluation phase in which the dataset and Gaussian process kernel with optimized hyperparameters are used to generate model data. The evaluation phase includes a nearest neighbour selection step. The method may include generating a model at a selected resolution.