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公开(公告)号:US11605021B1
公开(公告)日:2023-03-14
申请号:US16588245
申请日:2019-09-30
Applicant: Amazon Technologies, Inc.
Inventor: Vineet Khare , Saurabh Gupta , Yijie Zhuang , Bharathan Balaji , Runfei Luo , Siddhartha Agarwal
Abstract: Techniques for iterative model training and deployment for automated learning systems are described. A method of iterative model training and deployment for automated learning systems comprises generating training data based on inference data, provided by a first version of a model hosted at an endpoint of a machine learning service, and feedback data, received from a client application, using an identifier associated with the inference data and the feedback data, generating a second version of the model using the training data, and deploying the model to the endpoint of the machine learning service.
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公开(公告)号:US20230409584A1
公开(公告)日:2023-12-21
申请号:US18334091
申请日:2023-06-13
Applicant: Amazon Technologies, Inc.
Inventor: Ragav Venkatesan , Gurumurthy Swaminathan , Xiong Zhou , Runfei Luo , Vineet Khare
IPC: G06F16/2458 , G06F16/25 , G06F16/248 , G06N3/04 , H03M7/30 , G06N3/082
CPC classification number: G06F16/2474 , G06F16/252 , G06F16/248 , G06N3/04 , H03M7/30 , G06N3/082 , G06Q10/10
Abstract: Compression profiles may be searched for trained neural networks. An iterative compression profile search may be performed response to a search request. Different prospective compression profiles may be generated for trained neural networks according to a search policy. Performance of compressed versions of the trained neural networks according to the compression profiles may be tracked. The search policy may be updated according to an evaluation of the performance of the compression profiles for the compressed versions of the trained neural networks using compression performance criteria. When a search criteria is satisfied, a result for the compression profile search may be provided.
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公开(公告)号:US11755603B1
公开(公告)日:2023-09-12
申请号:US16831584
申请日:2020-03-26
Applicant: Amazon Technologies, Inc.
Inventor: Ragav Venkatesan , Gurumurthy Swaminathan , Xiong Zhou , Runfei Luo , Vineet Khare
IPC: G06F16/24 , G06F16/2458 , G06F16/25 , G06F16/248 , G06N3/04 , H03M7/30 , G06N3/082 , G06Q10/10
CPC classification number: G06F16/2474 , G06F16/248 , G06F16/252 , G06N3/04 , G06N3/082 , H03M7/30 , G06Q10/10
Abstract: Compression profiles may be searched for trained neural networks. An iterative compression profile search may be performed response to a search request. Different prospective compression profiles may be generated for trained neural networks according to a search policy. Performance of compressed versions of the trained neural networks according to the compression profiles may be tracked. The search policy may be updated according to an evaluation of the performance of the compression profiles for the compressed versions of the trained neural networks using compression performance criteria. When a search criteria is satisfied, a result for the compression profile search may be provided.
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公开(公告)号:US11809992B1
公开(公告)日:2023-11-07
申请号:US16836376
申请日:2020-03-31
Applicant: Amazon Technologies, Inc.
Inventor: Gurumurthy Swaminathan , Ragav Venkatesan , Xiong Zhou , Runfei Luo , Vineet Khare
Abstract: Neural networks with similar architectures may be compressed using shared compression profiles. A request to compress a trained neural network may be received and an architecture of the neural network identified. The identified architecture may be compared with the different network architectures mapped to compression profiles to select a compression profile for the neural network. The compression profile may be applied to remove features of the neural network to generate a compressed version of the neural network.
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公开(公告)号:US11501173B1
公开(公告)日:2022-11-15
申请号:US16831595
申请日:2020-03-26
Applicant: Amazon Technologies, Inc.
Inventor: Gurumurthy Swaminathan , Ragav Venkatesan , Xiong Zhou , Runfei Luo , Vineet Khare
Abstract: A compression policy to produce compression profiles for compressing trained machine learning models may be trained using reinforcement learning. An iterative reinforcement learning may be performed response to a search request. Different prospective compression profiles may be generated for received machine learning models according to a compression policy being trained. Performance of compressed versions of the trained neural networks according to the compression profiles may be caused using data sets used to train the machine learning models. The compression policy may be updated according to reward signal determined from an application of a reward function for performance criteria to performance results of the different versions of the machine learning models. When a search criteria is satisfied, the trained compression policy may be provided.
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