公开(公告)号：US11120364B1

公开(公告)日：2021-09-14

申请号：US16008903

申请日：2018-06-14

Applicant: Amazon Technologies, Inc.

Inventor： Sedat Gokalp ,  Tarun Gupta ,  Abhishek Dan

IPC: G06N20/00 ,  G06N5/04

Abstract: At an artificial intelligence system, respective status indicators for classifier training iterations are determined. A visualization data set comprising the status indicators is presented via an interactive programmatic interface. A training enhancement action, based at least partly on an objective associated with a status indicator, is initiated. The action includes selecting data items for which labeling feedback is to be obtained programmatically during one or more classifier training iterations. A classification model that is trained using the labeling feedback obtained as a result of the action is stored.

公开(公告)号：US11875230B1

公开(公告)日：2024-01-16

申请号：US16008897

申请日：2018-06-14

Applicant: Amazon Technologies, Inc.

Inventor： Sedat Gokalp

IPC: G06N20/00 ,  G06F16/248 ,  G06F16/25 ,  G06F16/242 ,  G06F16/2457

CPC classification number: G06N20/00 ,  G06F16/248 ,  G06F16/2423 ,  G06F16/24578 ,  G06F16/252

Abstract: At an artificial intelligence system, during a labeling feedback session, a visualization data set is presented via a programmatic interface. The visualization data set comprises a representation of data items for which labeling feedback is requested for generating a training set of a classifier. At least one of the data items is selected based on an estimated rank with respect to a metric associated with including the data item in a training set. During the session, respective labels for the data items and a filter criterion to be used to select additional data items are obtained. A classifier trained using the labels is stored.

公开(公告)号：US20240127575A1

公开(公告)日：2024-04-18

申请号：US18399005

申请日：2023-12-28

Applicant: Amazon Technologies, Inc.

Inventor： Sedat Gokalp ,  Tarun Gupta

IPC: G06V10/75 ,  G06F17/18 ,  G06F18/2113 ,  G06F18/214 ,  G06F18/2411 ,  G06N20/00

CPC classification number: G06V10/758 ,  G06F17/18 ,  G06F18/2113 ,  G06F18/2155 ,  G06F18/2411 ,  G06N20/00

Abstract: Learning iterations, individual ones of which include a respective bucket group selection phase and a class boundary refinement phase, are performed using a source data set whose records are divided into buckets. In the bucket group selection phase of an iteration, a bucket is selected for annotation based on output obtained from a classification model trained in the class boundary refinement phase of an earlier iteration. In the class boundary refinement phase, records of buckets annotated as positive-match buckets for a target class in the bucket group selection phase are selected for inclusion in a training set for a new version of the model using a model enhancement criterion. The trained version of the model is stored.

公开(公告)号：US11893772B1

公开(公告)日：2024-02-06

申请号：US16706472

申请日：2019-12-06

Applicant: Amazon Technologies, Inc.

Inventor： Sedat Gokalp ,  Tarun Gupta

IPC: G06F17/18 ,  G06V10/75 ,  G06N20/00 ,  G06F18/2113 ,  G06F18/214 ,  G06F18/2411

CPC classification number: G06V10/758 ,  G06F17/18 ,  G06F18/2113 ,  G06F18/2155 ,  G06F18/2411 ,  G06N20/00

Abstract: Learning iterations, individual ones of which include a respective bucket group selection phase and a class boundary refinement phase, are performed using a source data set whose records are divided into buckets. In the bucket group selection phase of an iteration, a bucket is selected for annotation based on output obtained from a classification model trained in the class boundary refinement phase of an earlier iteration. In the class boundary refinement phase, records of buckets annotated as positive-match buckets for a target class in the bucket group selection phase are selected for inclusion in a training set for a new version of the model using a model enhancement criterion. The trained version of the model is stored.

公开(公告)号：US11868436B1

公开(公告)日：2024-01-09

申请号：US16008894

申请日：2018-06-14

Applicant: Amazon Technologies, Inc.

Inventor： Sedat Gokalp ,  Abhishek Dan

IPC: G06F16/28 ,  G06F18/214 ,  G06N7/00 ,  G06N20/00 ,  G06F18/241 ,  G06F18/21

CPC classification number: G06F18/2148 ,  G06F18/2178 ,  G06F18/241 ,  G06N7/00 ,  G06N20/00

Abstract: At an artificial intelligence system, one or more classifier training iterations are performed until a training completion criterion is met. A particular iteration comprises obtaining, via an interactive interface, asynchronously with respect to the start of the iteration, class labels for data items identified in a previous iteration as candidates for labeling feedback. The particular iteration also comprises identifying, based on an analysis of classification predictions generated using classifiers trained using class labels obtained via the interface, another set of data items as candidates for labeling feedback. After the training criterion is met, a classifier trained using labels obtained during the iterations is stored.

公开(公告)号：US20230376857A1

公开(公告)日：2023-11-23

申请号：US18365927

申请日：2023-08-04

Applicant: Amazon Technologies, Inc.

Inventor： Sedat Gokalp

IPC: G06N20/00 ,  G06F16/25 ,  G06F16/242 ,  G06F16/248 ,  G06F16/2457

CPC classification number: G06N20/00 ,  G06F16/252 ,  G06F16/2423 ,  G06F16/248 ,  G06F16/24578

Abstract: At an artificial intelligence system, during a labeling feedback session, a visualization data set is presented via a programmatic interface. The visualization data set comprises a representation of data items for which labeling feedback is requested for generating a training set of a classifier. At least one of the data items is selected based on an estimated rank with respect to a metric associated with including the data item in a training set. During the session, respective labels for the data items and a filter criterion to be used to select additional data items are obtained. A classifier trained using the labels is stored.