International Business Machines Corporation

Eliran Roffe

Eitan D Farchi

Diptikalyan Saha

Tom Kolan

Idan Horowitz

Sandeep Hans

Hillel Mendelson

A method for performing an address translation context switch includes initializing a computer processor to a first context by storing information identifying the first context in a control register of the computer processor. The first context specifies a mapping of virtual addresses of instructions to physical memory addresses in a first memory area. Information identifying a second context is stored in a memory address translation independent storage, where the second context specifies mapping of virtual addresses of instructions to physical memory addresses in a second memory area. The information identifying the second context is written to the control register of the computer processor.

Validating address space context switches by loading an alternative address space from an address translation independent location

An example system includes a processor to receive a set of features, a set of relations between the features, and a set of target features. Each of the target features is associated with a number of the relations. The processor can generate a hypergraph based on the features and the relations. The processor also can select a subset of features based on a transitive closure of the hypergraph for each of the target features. The processor can transmit the selected subset of features.

Feature selection using hypergraphs

A method, system and computer program product, the method comprising: obtaining a first model trained upon cases and labels, the first model providing a prediction in response to an input case; obtaining a second model trained using the cases and indications whether a predictions of the first model are correct, the second model providing a correctness prediction for the first; determining a case for which the second model predicts that the first provides an incorrect prediction; further training the first model also on a first corpus including the case and a label, thereby improving performance of the first model; providing the case to the first model to obtain a first prediction; and further training the second model also on a second corpus including the case and a correctness label, the correctness label being 'correct' if the first prediction is equal to the label, thereby improving performance of the second model.

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Haifa, Israel

Eliran Roffe