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公开(公告)号：US12026598B2
公开(公告)日：2024-07-02
申请号：US18193023
申请日：2023-03-30
Applicant: Amazon Technologies, Inc.
Inventor： Yunzhe Tao , Sahika Genc , Tao Sun , Sunil Mallya Kasaragod
Abstract: A training system may create and train a machine learning model with knowledge transfer. The knowledge transfer may transfer knowledge that is acquired by another machine learning model that has been previously trained to the machine learning model that is under training. The knowledge transfer may include a combination of representation transfer and instance transfer, the two of which may be performed alternatingly. The instance transfer may further include a filter mechanism to selectively identify instances with a satisfactory performance to implement the knowledge transfer.
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公开(公告)号：US11847406B1
公开(公告)日：2023-12-19
申请号：US17217807
申请日：2021-03-30
Applicant: Amazon Technologies, Inc.
Inventor： Sunil Mallya Kasaragod , Yahor Pushkin , Saman Zarandioon , Graham Vintcent Horwood , Miguel Ballesteros Martinez , Yogarshi Paritosh Vyas , Yinxiao Zhang , Diego Marcheggiani , Yaser Al-Onaizan , Xuan Zhu , Liutong Zhou , Yusheng Xie , Aruni Roy Chowdhury , Bo Pang
IPC: G06F17/00 , G06F40/143 , G06F40/169 , G06N20/00 , G06F40/154 , G06F40/103 , G06F40/284
CPC classification number: G06F40/143 , G06F40/103 , G06F40/154 , G06F40/169 , G06F40/284 , G06N20/00
Abstract: Techniques for performing natural language processing (NLP) on semi-structured data are described. An exemplary method includes receiving a semi-structured document to perform NLP on using a trained NLP model; converting the semi-structured document into a secondary format, wherein the secondary format includes spatial information for tokens of the semi-structured document; flattening the converted, secondary formatted semi-structured document into a Unicode Transformation Format text file; performing NLP on the Unicode Transformation Format text file using the trained NLP model; and providing a result of the NLP to a requester.
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公开(公告)号：US11836577B2
公开(公告)日：2023-12-05
申请号：US16201830
申请日：2018-11-27
Applicant: Amazon Technologies, Inc.
Inventor： Sunil Mallya Kasaragod , Sahika Genc , Leo Parker Dirac , Bharathan Balaji , Eric Li Sun , Marthinus Coenraad De Clercq Wentzel
CPC classification number: G06N20/00 , B25J9/163 , B25J9/1605 , B25J9/1671 , G06F7/023 , G06F30/20 , G06F30/27 , G06N5/022
Abstract: A simulation management service receives a request to perform reinforcement learning for a robotic device. The request can include computer-executable code defining a reinforcement function for training a reinforcement learning model for the robotic device. In response to the request, the simulation management service generates a simulation environment and injects the computer-executable code into a simulation application for the robotic device. Using the simulation application and the computer-executable code, the simulation management service performs the reinforcement learning within the simulation environment.
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公开(公告)号：US11551652B1
公开(公告)日：2023-01-10
申请号：US16897094
申请日：2020-06-09
Applicant: Amazon Technologies, Inc.
Inventor： Ambika Pajjuri , Nagajyothi Nookula , Rahul Suresh , Sunil Mallya Kasaragod , Richard Lee , Hsin Chieh Chen
Abstract: Indications of sample machine learning models which create synthetic content items are provided via programmatic interfaces. A representation of a synthetic content item produced by one of the sample models in response to input obtained from a client of a provider network is presented. In response to a request from the client, a machine learning model is trained to produce additional synthetic content items.
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公开(公告)号：US11429762B2
公开(公告)日：2022-08-30
申请号：US16201872
申请日：2018-11-27
Applicant: Amazon Technologies, Inc.
Inventor： Sunil Mallya Kasaragod , Sahika Gene , Leo Parker Dirac , Bharathan Balaji , Eric Li Sun , Marthinus Coenraad De Clercq Wentzel , Brian James Townsend , Pramod Ravikumar Kumar
Abstract: A simulation workflow manager obtains a set of parameters for simulation of a system and training of a reinforcement learning model for optimizing an application of the system. In response to obtaining the set of parameters, the simulation workflow manager configures a first compute node that includes a training application for training the reinforcement learning model. The simulation workflow manager also configures a second compute note with a simulation application to perform the simulation of the system in a simulation environment. Data is generated through execution of the simulation in the second compute node that is provided to the first compute node to cause the training application to use the data to train the reinforcement learning model.
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Patent Agency Ranking
公开(公告)号：US12265924B1
公开(公告)日：2025-04-01
申请号：US16908486
申请日：2020-06-22
Applicant: Amazon Technologies, Inc.
Inventor： Tao Sun , Yunzhe Tao , Sahika Genc , Sunil Mallya Kasaragod , Kaiqing Zhang
Abstract: Techniques for robust multi-agent reinforcement learning (MARL) are described. An exemplary method includes initializing a plurality of parameters for a plurality of agents including at least policy parameters and action-value (Q) parameters; performing robust multi-agent reinforcement learning to learn polices for the agents, wherein in the learned polices no agent has an incentive to deviate, the agents include an implicit agent that is to select a worst-case at any given time during the learning process; and at least one agent utilizing its learned policy.
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公开(公告)号：US12061956B1
公开(公告)日：2024-08-13
申请号：US17037114
申请日：2020-09-29
Applicant: Amazon Technologies, Inc.
Inventor： Yahor Pushkin , Sunil Mallya Kasaragod , Sravan Babu Bodapati
CPC classification number: G06N20/00 , G06F11/3688 , G06F11/3692
Abstract: Techniques for utilizing a federated learning service are described. An exemplary method includes causing a development of a deployable machine learning model using at least two devices, the development of the deployable machine learning model including: providing an initial machine learning model or algorithm to the at least two devices external to the provider network, causing each of the at least two devices external to the provider network to locally train the initial machine learning model or algorithm using training data to each generate a modified version of the initial machine learning model, determining updates between the initial model and the generated modified versions of the initial machine learning model, and applying the determined updates to the initial model to generate the candidate machine learning model.
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公开(公告)号：US11900244B1
公开(公告)日：2024-02-13
申请号：US16588789
申请日：2019-09-30
Applicant: Amazon Technologies, Inc.
Inventor： Sahika Genc , Sravan Babu Bodapati , Tao Sun , Sunil Mallya Kasaragod
IPC: G06N3/08 , G05D1/02 , G06F17/16 , G06F16/904
CPC classification number: G06N3/08 , G05D1/0221 , G06F16/904 , G06F17/16 , G05D2201/0213
Abstract: A data source configured to provide a representation of an environment of one or more agents is identified. Using a data set obtained from the data source, a neural network-based reinforcement learning model with one or more attention layers is trained. Importance indicators generated by the attention layers are used to identify actions to be initiated by an agent. A trained version of the model is stored.
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公开(公告)号：US20230252355A1
公开(公告)日：2023-08-10
申请号：US18193023
申请日：2023-03-30
Applicant: Amazon Technologies, Inc.
Inventor： Yunzhe Tao , Sahika Genc , Tao Sun , Sunil Mallya Kasaragod
Abstract: A training system may create and train a machine learning model with knowledge transfer. The knowledge transfer may transfer knowledge that is acquired by another machine learning model that has been previously trained to the machine learning model that is under training. The knowledge transfer may include a combination of representation transfer and instance transfer, the two of which may be performed alternatingly. The instance transfer may further include a filter mechanism to selectively identify instances with a satisfactory performance to implement the knowledge transfer.
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公开(公告)号：US20230237980A1
公开(公告)日：2023-07-27
申请号：US18151359
申请日：2023-01-06
Applicant: Amazon Technologies, Inc.
Inventor： Ambika Pajjuri , Nagajyothi Nookula , Rahul Suresh , Sunil Mallya Kasaragod , Richard Lee , Hsin Chieh Chen
CPC classification number: G10H1/0025 , G06N3/088 , G06N3/045 , G10H2220/221 , G10H2220/116 , G10H2210/111
Abstract: Indications of sample machine learning models which create synthetic content items are provided via programmatic interfaces. A representation of a synthetic content item produced by one of the sample models in response to input obtained from a client of a provider network is presented. In response to a request from the client, a machine learning model is trained to produce additional synthetic content items.
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