Automation is the key to build efficient workflows with minimum effort consumption. However, there is a large gap in workflow synthesis for automated AI application development. Computer vision workflow synthesis largely rely on domain expert due to lack of generalization over solution search space for given goal. This search space for creating suitable solution(s) using available algorithms is quite vast, which makes exploratory work of solution building a time-, effort- and intellect intensive endeavor. Embodiments of the present disclosure provide system and method for goal-driven algorithm selection approach for building computer vision workflows on the fly. The system generates one or more task workflows with associated success probability depending on initial conditions and input natural language goal query by combining various image processing algorithms. Symbolic AI planning is aided by Reinforcement Learning to recommend optimal workflows that are robust and adaptive to changes in the environment.

Methods for generating data models using a generative adversarial network can begin by receiving a data model generation request by a model optimizer from an interface. The model optimizer can provision computing resources with a data model. As a further step, a synthetic dataset for training the data model can be generated using a generative network of a generative adversarial network, the generative network trained to generate output data differing at least a predetermined amount from a reference dataset according to a similarity metric. The computing resources can train the data model using the synthetic dataset. The model optimizer can evaluate performance criteria of the data model and, based on the evaluation of the performance criteria of the data model, store the data model and metadata of the data model in a model storage. The data model can then be used to process production data.

Methods for generating data models using a generative adversarial network can begin by receiving a data model generation request by a model optimizer from an interface. The model optimizer can provision computing resources with a data model. As a further step, a synthetic dataset for training the data model can be generated using a generative network of a generative adversarial network, the generative network trained to generate output data differing at least a predetermined amount from a reference dataset according to a similarity metric. The computing resources can train the data model using the synthetic dataset. The model optimizer can evaluate performance criteria of the data model and, based on the evaluation of the performance criteria of the data model, store the data model and metadata of the data model in a model storage. The data model can then be used to process production data.

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to determine a first action based on inputting sensor data to a deep reinforcement learning neural network and transform the first action to one or more first commands. One or more second commands can be determined by inputting the one or more first commands to control barrier functions and transforming the one or more second commands to a second action. A reward function can be determined by comparing the second action to the first action. The one or more second commands can be output.

A method for predicting user purchase by a user of a first site includes: selecting a distribution representing a probability distribution (PD) of inter-purchase-times (IPTs) across the first site and a second other site for each user, assigning each purchase of each user to one of the first site and the second site according to a Stochastic model, combining the selected PD with the Stochastic model to generate a PD of IPTs for only the first online site, estimating parameters of the probability distribution of IPTs for the first site by applying a Statistical modeling approach to features of each user, applying a sequence of observed IPTs of a given user for the first site and the parameters of the given user to the selected distribution to generate a probability, and determining whether the next purchase occurs on the second site based on the probability.

A method for predicting user purchase by a user of a first site includes: selecting a distribution representing a probability distribution (PD) of inter-purchase-times (IPTs) across the first site and a second other site for each user, assigning each purchase of each user to one of the first site and the second site according to a Stochastic model, combining the selected PD with the Stochastic model to generate a PD of IPTs for only the first online site, estimating parameters of the probability distribution of IPTs for the first site by applying a Statistical modeling approach to features of each user, applying a sequence of observed IPTs of a given user for the first site and the parameters of the given user to the selected distribution to generate a probability, and determining whether the next purchase occurs on the second site based on the probability.

Search elements are extracted from requirement definitions of a requirement management tool for managing a project. The search elements may be extracted using natural language processing. The search elements are used to identify source code from source code repositories. Machine learning correlates the requirement definitions to source code subject matter. The extracted source code is confirmed by a stakeholder of the requirement management tool.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for accounting for long-tail training data.

An apparatus identifies partial tensor data that contributes to machine learning using tensor data in a tensor format obtained by transforming training data having a graph structure. Based on the partial tensor data and the training data, the apparatus generates expanded training data to be used in the machine learning by expanding the training data.

The present invention is to provide a multi-class classification method, a multi-class classification program, and a multi-class classification device which can robustly and highly accurately classify a sample having a plurality of feature amounts into any of a plurality of classes based on a value of a part of the selected feature amount. In addition, the present invention is to provide a feature amount selection method, a feature amount selection program, a feature amount selection device, and a feature amount set used for such multi-class classification. The present invention handles a multi-class classification problem involving feature amount selection. The feature amount selection is a method of literally selecting in advance a feature amount needed for each subsequent processing (particularly, the multi-class classification in the present invention) from among a large number of feature amounts included in a sample. The multi-class classification is a discrimination problem that decides which of a plurality of classes a given unknown sample belongs to.