International Business Machines Corporation

University of Massachusetts

Sanjeeb Dash

Lior Horesh

Oktay Gunluk

Jayant R Kalagnanam

Giacomo Nannicini

Chandrasekhara K Reddy

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

An automated method optimally designs plates to satisfy an order book at a steel plant so as to maximize the yield of the plates designed while using capacity fully to reduce the production of surplus slabs or plates, and satisfy order deadlines. Our method consists of four main components: (1) mother plate design, (2) slab design, (3) cast design, and (4) material allocation. A column generation framework for mother plate design is used where the problem is decomposed into a master problem and a subproblem. The master problem is used to evaluate packing patterns that should be used to fulfill the order book and the subproblem generates potential one-dimensional and two-dimensional feasible packing patterns as candidates to be evaluated by the master problem. The solution to the master problem produces a list of mother plates that need to be produced. These mother plates are transformed into candidate slabs, which are represented via an interval graph. The maximal cliques in the interval graph&#x2014;maximal subsets of slabs which can be grouped together to form casts&#x2014;are enumerated to generate candidate casts, from which redesign information is sent to the plate/slab design processes. The cast design process is integrated with the plate design/slab design processes in the sense that information is passed from the plate/slab design processes to the cast design process and vice versa. Finally, our method has a material allocation component to assign order plates to inventory mother-plates and slabs.

Method for production design and operations scheduling for plate design in the steel industry

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Croton on Hudson, NY, United States of America

Sanjeeb Dash