International Business Machines Corporation

At&t Intellectual Property I, L.p.

At+t Corp.

University of Massachusetts

Oktay Gunluk

Joel Leonard Wolf

Laura Wynter

James R H Challenger

Kirsten Weale Hildrum

Richard Pervin King

Deepak Rajan

Nikhil Bansal

David Tao

Lisa Karen Fleischer

Lior Horesh

Sanjeeb Dash

Giacomo Nannicini

Shashanka Ubaru

Parikshit Ram

Alexander Gray

Arya Mazumdar

A processor may include a set of primitive operators, receive a set of data-driven operators, at least one of the set of data-driven operators including a machine learning model, and receive an input-output data pair set. Based on a grammar specifying rules for linking the set of primitive operators and the set of data-driven operators, the processor may search among the set of primitive operators and the set of data-driven operators to find a symbolic model that fits the input-output data set.

Free-form integration of machine learning model primitives

A computer-implemented method for automatic multilabel classification includes receiving a label matrix Y for multiple training instances. The label matrix Y includes multiple labels, each label representing a respective category. The method further includes computing an intermediate matrix YY, where Yis a transpose of the label matrix Y. The method further includes computing a basis matrix H by a non-negative matrix factorization of the intermediate matrix YY. The method further includes generating a group testing matrix A by sampling the basis matrix H. The method further includes generating, for each training instance from the training instances, a reduced label vector z by computing a product of the group testing matrix A and a label vector y for respective training instance from the label matrix Y. The method further includes predicting multiple labels associated with an input based on the reduced label vector z.

Automatic multilabel classification using machine learning

A method of choosing jobs to run in a stream based distributed computer system includes determining jobs to be run in a distributed stream-oriented system by deciding a priority threshold above which jobs will be accepted, below which jobs will be rejected. Overall importance is maximized relative to the priority threshold based on importance values assigned to all jobs. System constraints are applied to ensure jobs meet set criteria.

Method and apparatus for assigning candidate processing nodes in a stream-oriented computer system

The present invention discloses a method of determining a route for transmitting a signal through a network. The method comprises obtaining and storing network and demand data. The demand data is processed using a shortest path routing method to obtain an initial route and the network data is updated accordingly. An initial cost based on the initial route is computed. The initial route is returned to the network and the network data is again updated with regard to increasing spare capacity. The demand data is re-processed using a constrained diverse shortest path routing method to obtain a final route until a stop criterion is satisfied. A final cost based on the final route is computed.

Method for routing optical signals with diversity requirements

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New York, NY, United States of America

Oktay Gunluk