International Business Machines Corporation

Lin Ju

Patrick Joseph O'Sullivan

Dakuo Wang

John Dillon Eversman

Voranouth Supadulya

Edith H Stern

Barry Edward Willner

Robert Cameron Weir

Jing James Xu

Fred Raguillat

Chuang Gan

Theodoros Salonidis

Jun Song Wang

Ming Tan

Ji Hui Yang

Thanh Lam Hoang

Craig Reginald Tomlyn

Gregory Bramble

Arunima Chaudhary

Alex Swain

Jishuo Yang

Nicholas Mazzitelli

Embodiments for providing automated machine learning visualization. Machine learning tasks, transformers, and estimators may be received into one or more machine learning composition modules. The machine learning composition modules generate one or more machine learning models. A machine learning model pipeline is a sequence of transformers and estimators and an ensemble of machine learning pipelines are an ensemble of machine learning pipelines. A machine learning model pipeline, an ensemble of a plurality of machine learning model pipelines, or a combination thereof, along with corresponding metadata, may be generated using the machine learning composition modules. Metadata may be extracted from the machine learning model pipeline, the ensemble of a plurality of machine learning model pipelines, or combination thereof. An interactive visualization graphical user interface of the machine learning model pipeline, the ensemble of a plurality of machine learning model pipelines, or combination thereof, and the extracted metadata may be generated.

Automated artificial intelligence radial visualization

In accordance with an embodiment of the invention, a method is provided for personalizing machine learning models for users of an automated machine learning system, the machine learning models being generated by an automated machine learning system. The method includes obtaining a first set of datasets for training first, second, and third neural networks, inputting the training datasets to the neural networks, tuning hyperparameters for the first, second, and third neural networks for testing and training the neural networks, inputting a second set of datasets to the trained neural networks and the third neural network generating a third output data including a relevance score for each of the users for each of the machine learning models, and displaying a list of machine learning models associated with each of the users, with each of the machine learning models showing the relevance score.

Personalized automated machine learning

Embodiments for managing feature engineering with relational data are provided. A graphical user interface (GUI) that provides a user with the ability to upload a plurality of tables, select joins between the plurality of tables, and select keys for the joins is provided. Responsive to receiving user input indicative of selecting joins between the plurality of tables and selecting keys for the joins utilizing the GUI, the user selections are automatically validated and actions associated with at least some of the plurality of tables are dynamically performed based on the user selections. Information associated with the user selections and the validating is provided. The information includes a recommendation to link a third key in the at least some of the plurality of tables to a fourth key in the at least some of the plurality of tables.

Methods and systems for visual management of relational data

Annotating a document in a data processing system, wherein the document includes a first content section and a data structure, can include receiving a request to annotate the document, wherein the request comprises an annotation, and an identification of the document to annotate, and creating a second content section comprising the annotation. The data structure can be updated with a reference to the second content section thereby making the second content section available as an annotation in association with the document.

Electronic document annotation

An event scheduling request is received, and a disruption factor is associated with each of a plurality of invitees. The disruption factor is based upon, at least in part, an event parameter and at least one invitee attribute. An event is scheduled based upon, at least in part, the disruption factors associated with each of the plurality of invitees.

Motivating equitable behavior in calendar events

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Markham, Canada

Lin Ju