-
公开(公告)号：US20200151606A1
公开(公告)日：2020-05-14
申请号：US16742768
申请日：2020-01-14
Applicant: Amazon Technologies, Inc.
IPC: G06N20/00
Abstract: A first set of execution platforms is deployed for a set of operations of a training phase of a machine learning model. Prior to the completion of the training phase, a triggering condition for deployment of a different set of execution platforms is detected. The different set of execution platforms is deployed for a subsequent set of training phase operations.
-
公开(公告)号：US09704054B1
公开(公告)日：2017-07-11
申请号：US14870575
申请日：2015-09-30
Applicant: Amazon Technologies, Inc.
Inventor： Marshall Friend Tappen , Avinash Aghoram Ravichandran , Hakan Boyraz , Baoyuan Liu
CPC classification number: G06K9/46 , G06K9/4628 , G06K9/6218 , G06K9/6267 , G06K9/6281
Abstract: Image classification and related imaging tasks performed using machine learning tools may be accelerated by using one or more of such tools to associate an image with a cluster of such labels or categories, and then to select one of the labels or categories of the cluster as associated with the image. The clusters of labels or categories may comprise labels that are mutually confused for one another, e.g., two or more labels or categories that have been identified as associated with a single image. By defining clusters of labels or categories, and configuring a machine learning tool to associate an image with one of the clusters, processes for identifying labels or categories associated with images may be accelerated because computations associated with labels or categories not included in the cluster may be omitted.
-
公开(公告)号：US11715033B2
公开(公告)日：2023-08-01
申请号：US16742768
申请日：2020-01-14
Applicant: Amazon Technologies, Inc.
Abstract: A first set of execution platforms is deployed for a set of operations of a training phase of a machine learning model. Prior to the completion of the training phase, a triggering condition for deployment of a different set of execution platforms is detected. The different set of execution platforms is deployed for a subsequent set of training phase operations.
-
公开(公告)号：US09697608B1
公开(公告)日：2017-07-04
申请号：US14301599
申请日：2014-06-11
Applicant: Amazon Technologies, Inc.
Inventor： Oleg Rybakov , Avinash Aghoram Ravichandran , Daniel Bibireata , Ajay Kumar Mishra , Wei Zhang
CPC classification number: G06T7/254 , G06K9/00456 , G06K9/00671 , G06K9/46 , G06K9/6211 , H04N7/18 , H04N7/183
Abstract: A computing device can be configured to analyze information, such as frames captured in a video by a camera in the computing device, to determine locations of objects in captured frames using a scene-based tracking approach without individually having to track the identified objects across the captured frames. The computing device can track scenes, a global planar surface, across newly captured frames and the changes to (or transformation) the scene can be used to determine updated locations for objects that were identified in previously captured frames. Changes to the scene between frames can be measured using various techniques for estimating homographies. An updated location for the particular object in the currently captured frame can be determined by adjusting the location of the object, as determined in the previously captured frame, with respect to the transformation of the scene between the previously captured frame and the currently captured frame.
-
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
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.
-
Patent Agency Ranking
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.
-
公开(公告)号：US11429813B1
公开(公告)日：2022-08-30
申请号：US16697662
申请日：2019-11-27
Applicant: Amazon Technologies, Inc.
Inventor： Avinash Aghoram Ravichandran , Rahul Bhotika , Stefano Soatto , Pietro Perona , Hao Yang
Abstract: This disclosure describes automatically selecting and training one or more models for image recognition based upon training and testing (validation) data provided by a user. A service provider network includes a recognition service that may use models to process images and videos to recognize objects in the images and videos, features on the objects in the images and videos, and/or locate objects in the images and videos. The service provider network also includes a model selection and training service that may select one or more modeling techniques based on the objectives of the user and/or the amount of data provided by the user. Based on the selected modeling technique, the model selection and training service selects and trains one or more models for use by the recognition service to process images and videos using the training data. The trained model may be tested and validated using the testing data.
-
公开(公告)号：US10963754B1
公开(公告)日：2021-03-30
申请号：US16144927
申请日：2018-09-27
Applicant: Amazon Technologies, Inc.
Inventor： Avinash Aghoram Ravichandran , Paulo Ricardo dos Santos Mendonca , Rahul Bhotika , Stefano Soatto
Abstract: Techniques for training an embedding using a limited training set are described. In some examples, the embedding is trained by generating a plurality of vectors from a random sample of the limited set of training data classes using a layer of the particular machine learning classification model, randomly selecting samples from the plurality of vectors into a set of samples, computing at least one distance for each sampled class from a center parameter for the class using the set of samples, generating a discrete probability distribution over the classes for a query point based on distances to a center parameter for each of the classes in the embedding space, calculating a loss value for the modified prototypical network, the calculation of the loss value being for a fixed geometry of the embedding space and including a measure of the difference between distributions, and back propagating.
-
公开(公告)号：US10540608B1
公开(公告)日：2020-01-21
申请号：US14720166
申请日：2015-05-22
Applicant: Amazon Technologies, Inc.
Abstract: A first set of execution platforms is deployed for a set of operations of a training phase of a machine learning model. Prior to the completion of the training phase, a triggering condition for deployment of a different set of execution platforms is detected. The different set of execution platforms is deployed for a subsequent set of training phase operations.
-
公开(公告)号：US09171195B1
公开(公告)日：2015-10-27
申请号：US14305492
申请日：2014-06-16
Applicant: Amazon Technologies, Inc.
CPC classification number: G06K9/6807 , G06K9/00201
Abstract: An object recognition system may recognize an object in a query image by matching the image to one or more images in a database. The database may include images corresponding to multiple viewpoints of a particular device. Key points of the query image are compared to key points in the database images. Database images with many overlapping key points to the query image are selected as potential matches. The geometry of objects in the potential matches is verified to the geometry of the object in the query image to determine if the overlapping key points have a similar geographic relationship to each other across images. Objects in geometrically verified database images may be selected as potentially matching objects to the object in the query image. When a potential matching image is found, the system may confirm the match by performing matching with a second image of the object.
Abstract translation: 对象识别系统可以通过将图像与数据库中的一个或多个图像相匹配来识别查询图像中的对象。 数据库可以包括对应于特定设备的多个视点的图像。 将查询图像的要点与数据库图像中的要点进行比较。 选择具有查询图像的许多重叠关键点的数据库图像作为潜在匹配。 潜在匹配中的对象的几何结构被验证为查询图像中的对象的几何，以确定重叠的关键点是否在图像之间彼此具有相似的地理关系。 可以将几何校验的数据库图像中的对象选择为与查询图像中的对象潜在匹配的对象。 当找到潜在的匹配图像时，系统可以通过与对象的第二图像进行匹配来确认匹配。
-
-
-
-
-
-
-
-
-
Search Results
10
records
(took 0.016 seconds)
