公开(公告)号：US12147878B2

公开(公告)日：2024-11-19

申请号：US17106026

申请日：2020-11-27

Applicant: Amazon Technologies, Inc.

Inventor： Barath Balasubramanian ,  Rahul Bhotika ,  Niels Brouwers ,  Ranju Das ,  Prakash Krishnan ,  Shaun Ryan James McDowell ,  Anushri Mainthia ,  Rakesh Madhavan Nambiar ,  Anant Patel ,  Avinash Aghoram Ravichandran ,  Joaquin Zepeda Salvatierra ,  Gurumurthy Swaminathan

IPC: G06N3/091 ,  G06F16/23 ,  G06F18/21 ,  G06F18/214 ,  G06N3/088 ,  G06N5/04 ,  G06N20/00 ,  G06T7/00

Abstract: Techniques for feedback-based training may include selecting a scoring machine learning model based at least in part on a test metric, and applying the model on an unlabeled dataset to generate, per dataset item of the unlabeled dataset, a prediction and an importance ranking score for the prediction. Techniques for feedback-based training may further include selecting, based on the importance ranking scores, a result of the application of the model on the unlabeled dataset, providing the result and requesting feedback on the result via a graphical user interface, receiving the feedback via the graphical user interface, adding data from the unlabeled dataset into a training dataset when the feedback indicates a verified result, and retraining the model using the training dataset with the data added from the unlabeled dataset to generate a retrained model.

公开(公告)号：US11983243B2

公开(公告)日：2024-05-14

申请号：US17106023

申请日：2020-11-27

Applicant: Amazon Technologies, Inc.

Inventor： Barath Balasubramanian ,  Rahul Bhotika ,  Niels Brouwers ,  Ranju Das ,  Prakash Krishnan ,  Shaun Ryan James Mcdowell ,  Anushri Mainthia ,  Rakesh Madhavan Nambiar ,  Anant Patel ,  Avinash Aghoram Ravichandran ,  Joaquin Zepeda Salvatierra ,  Gurumurthy Swaminathan

IPC: G06N20/00 ,  G06F9/451 ,  G06F18/21 ,  G06F18/214 ,  G06N3/088 ,  G06N3/09 ,  G06V10/70 ,  G06V10/774 ,  G06V10/778 ,  H04L9/40

CPC classification number: G06F18/2148 ,  G06F9/451 ,  G06F18/2155 ,  G06F18/2178 ,  G06N3/088 ,  G06N3/09 ,  G06N20/00 ,  G06V10/70 ,  G06V10/7753 ,  G06V10/7784 ,  H04L63/1425 ,  G06T2207/20081

Abstract: Techniques for anomaly detection are described. An exemplary method includes receiving one or more requests to train an anomaly detection machine learning model using feedback-based training, the request to indicate one or more of a type of analysis to perform, a model selection indication, and a configuration for a training dataset; training the anomaly detection machine learning model according to the one or more requests using the training data; performing feedback-based training on the trained anomaly detection machine learning model; and using the retrained anomaly detection machine learning model.