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公开(公告)号：US20230102152A1
公开(公告)日：2023-03-30
申请号：US17484104
申请日：2021-09-24
IPC分类号： G06Q10/06 , G06F16/215 , G06K9/62 , G06F11/34 , G06N20/00
摘要： A system, program product, and method for automatic detection of data drift in a data set are presented. The method includes determining changes to relations in the data set through generating baseline and production data sets. The method further includes generating a production data set with some inserted data distortion, and defining, for a plurality of features in the baseline data set, potential relations for participant features. The method also includes determining a first likelihood and a second likelihood of each potential relation in the baseline and production data sets, respectively, for the participant features. The method further includes comparing each first likelihood with each second likelihood, generating a comparison value that is compared with a threshold value, and determining, subject to the comparison value exceeding the threshold value, the potential relation in the baseline data set does not describe a relation in the production data set.
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公开(公告)号：US12045317B2
公开(公告)日：2024-07-23
申请号：US17533898
申请日：2021-11-23
IPC分类号： G06V10/00 , G06F18/2323 , G06F18/2411 , G06N20/20
CPC分类号： G06F18/2411 , G06F18/2323 , G06N20/20
摘要： 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.
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公开(公告)号：US11710068B2
公开(公告)日：2023-07-25
申请号：US16693303
申请日：2019-11-24
发明人： Eitan Farchi , Eliran Roffe
摘要： 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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公开(公告)号：US20210158205A1
公开(公告)日：2021-05-27
申请号：US16693303
申请日：2019-11-24
发明人： Eitan Farchi , Eliran Roffe
摘要： 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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