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公开(公告)号：US20220036247A1
公开(公告)日：2022-02-03
申请号：US16944324
申请日：2020-07-31
Applicant: Oath Inc.
Inventor： Gal Lalouche , Ran Wolff
Abstract: The present teaching relates to method, system, medium, and implementations for machine learning. A training sample is first received from a source. A prediction is generated according to the training sample and based on one or more parameters associated with a model. A metric characterizing the prediction is also determined. The prediction and the metric are transmitted to the source to facilitate a determination on whether a ground truth label for the training sample is to be provided. When the ground truth label is received from the source, the one or more parameters of the model are updated based on the prediction and the ground truth label.
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公开(公告)号：US20220036248A1
公开(公告)日：2022-02-03
申请号：US16944415
申请日：2020-07-31
Applicant: Oath Inc.
Inventor： Gal Lalouche , Ran Wolff
Abstract: The present teaching relates to method, system, medium, and implementations for machine learning. A check is performed on a level of available bidding currency for bidding a training sample that is used to train a model via machine learning. A bid in an amount within the available bidding currency is sent, to a source of the training sample, for the training sample. The training sample is received from the source when the bid is successful. A prediction is then generated in accordance with the training sample based on one or more parameters associated with the model and is sent to the source.
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公开(公告)号：US20220036249A1
公开(公告)日：2022-02-03
申请号：US16944459
申请日：2020-07-31
Applicant: Oath Inc.
Inventor： Gal Lalouche , Ran Wolff
Abstract: The present teaching relates to method, system, medium, and implementations for machine learning. A training sample is sent to an expert for training a model representative of the expert. A prediction is received, which is generated by the expert in accordance with the training sample and based on one or more parameters associated with the model. A metric with respect to the prediction characterizing the prediction received from the expert is analyzed. When the metric satisfies a first criterion, a ground truth label associated with the training sample is sent to the expert to facilitate the training.
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公开(公告)号：US20220036138A1
公开(公告)日：2022-02-03
申请号：US16944503
申请日：2020-07-31
Applicant: Oath Inc.
Inventor： Gal Lalouche , Ran Wolff
Abstract: The present teaching relates to method, system, medium, and implementations for machine learning. A bid is received, from an expert during training, for a training sample with an amount within a level of available bidding currency associated with the expert. The training sample is used for training a model associated with the expert. It is determined whether the expert is among at least one winner selected based on bids from one or more experts. If the expert is among the at least one winner, the training sample is sent to the expert. The at least one winner is selected based on one or more criteria aiming at expert diversification.
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