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公开(公告)号：US20240211812A1
公开(公告)日：2024-06-27
申请号：US18145912
申请日：2022-12-23
发明人： Gursimran SINGH , Xinglu WANG , Yong ZHANG , Mohammad AKBARI
IPC分类号： G06N20/20 , G06F16/2457
CPC分类号： G06N20/20 , G06F16/24578
摘要： Methods and systems for selecting a target model for an unlabeled dataset of a dataset provider, the target model for generating labels for the unlabeled dataset. The method comprises acquiring the unlabeled dataset from the dataset provider; acquiring a first candidate model from a first model provider and a second candidate model from a second model provider, generating a first usefulness score for the first candidate model and a second usefulness score for the second candidate model using the unlabeled dataset, the first and second usefulness scores being indicative of likelihood that the first and second candidate models generate accurate labels for the unlabeled dataset respectively; selecting the first candidate model as the target model using the first usefulness score and the second usefulness score; and causing generation of the labels from the unlabeled dataset using the target model.
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公开(公告)号：US20230110925A1
公开(公告)日：2023-04-13
申请号：US18074915
申请日：2022-12-05
IPC分类号： G06N5/02
摘要： Method and system for predicting a label for an input sample. A first label is predicted for the input sample using a first machine learning (ML) model that has been trained to map samples to a first set of labels; If the first label satisfies prediction accuracy criteria it is outputted as the predicted label for the input sample; if the first label does not satisfy the prediction accuracy criteria, a second label is predicted for the input sample using a second ML model that has been trained to map samples to a second set of labels that includes the first set of labels and a set of additional labels, and the second label is outputted as the predicted label for the input sample.
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