International Business Machines Corporation

Kiran A Kate

Martin J Hirzel

Avraham Ever Shinnar

Jayant R Kalagnanam

Julian Timothy Dolby

Kavitha Srinivas

Chandrasekhara K Reddy

Louis Mandel

Zhiguo Li

A computer-implemented method according to one embodiment includes identifying a machine learning pipeline and a plurality of training data batches; creating a plurality of tasks, based on the machine learning pipeline; and determining an order in which the plurality of tasks is executed, utilizing a resource usage-aware approach.

Performing batched training for machine-learning pipelines

Embodiments of the present invention provide a computer system a computer program product, and a method that comprises analyzing identified data for a determined conversion of the identified data, wherein the identified data is input data stored on an external database; automatically converting the analyzed data to a uniform domain by mapping a data route within the analyzed data, predicting a plurality of outcomes based on an application of a plurality of scenarios associated with the mapped data route, ranking the predicted outcomes based on a positive match percentage for the analyzed data, and converting the analyzed data associated with at least one ranked outcome using a relational algebra algorithm; and dynamically transmitting the converted, analyzed data into at least one section of a machine learning data pipeline.

Automatic conversion of data within data pipeline

Embodiments are disclosed for a method. The method includes identifying a prefix updated by a searcher of a machine learning model. The machine learning model is configured to generate source code in a programming language. The method also includes determining whether the prefix violates a semantic correctness property of the programming language. Additionally, the method includes instructing the searcher, in response to the determination, to prune the prefix from a set of prefixes under consideration by the searcher.

Reducing semantic errors in code generated by machine learning models

A sub-process sequence is identified from a temporal dataset. Based on time information, predictors are categorized as being available or not available during time periods. The predictors are used to make predictions of quantities that will occur in a future time period. The predictors are grouped into groups of a sequence of sub-processes, each including a grouping of one or more of the predictors. Information is output that allows a human being to modify the groups. The groups are finalized, responsive to any modifications. Prediction models are extracted based on dependencies between groups and sub-processes. A final predication model is determined based on a prediction model from the prediction models that best meets criteria. A dependency graph is generated based on the final prediction model. Information is output to display the final dependency graph for use by a user to adjust or not adjust elements of the sequential process.

System for automatic deduction and use of prediction model structure for a sequential process dataset

A method for automatically constructing and organizing a navigation graph includes receiving input data including a plurality of reports, at least two of the reports including plots, extracting a plurality of variables from the plots, building a knowledge graph from the input, wherein each node of the knowledge graph is associated with an individual one of the plots and an edge is added between two of the nodes sharing at least one of the variables in common, adding an edge weight to each of the edges of the knowledge graph, and organizing the nodes of the knowledge graph for navigation, wherein the knowledge graph is displayed in a user interface.

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Chappaqua, NY, United States of America

Kiran A Kate