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公开(公告)号:US20230017505A1
公开(公告)日:2023-01-19
申请号:US17375960
申请日:2021-07-14
Applicant: Google LLC
Inventor: Aditya Krishna Menon , Sanjiv Kumar , Himanshu Jain , Andreas Veit , Ankit Singh Rawat , Gayan Sadeep Jayasumana Hirimbura Matara Kankanamge
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for accounting for long-tail training data.
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公开(公告)号:US20220335274A1
公开(公告)日:2022-10-20
申请号:US17721292
申请日:2022-04-14
Applicant: Google LLC
Inventor: Ankit Singh Rawat , Manzil Zaheer , Aditya Krishna Menon , Sanjiv Kumar , Amr Ahmed
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for multi-stage computationally-efficient inference using a first and second neural network.
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公开(公告)号:US20240135254A1
公开(公告)日:2024-04-25
申请号:US18488951
申请日:2023-10-17
Applicant: Google LLC
Inventor: Harikrishna Narasimhan , Wittawat Jitkrittum , Aditya Krishna Menon , Ankit Singh Rawat , Sanjiv Kumar
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for post-hoc deferral for classification tasks. In particular, a system can perform either post-hoc threshold correction or post-hoc rejector training to account for the cost of deferring model inputs to an expert system for classification.
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公开(公告)号:US11676033B1
公开(公告)日:2023-06-13
申请号:US16812160
申请日:2020-03-06
Applicant: Google LLC
Inventor: Aditya Krishna Menon , Ankit Singh Rawat , Sashank Jakkam Reddi , Sanjiv Kumar
Abstract: A method for training a machine learning model, e.g., a neural network, using a regularization scheme is disclosed. The method includes generating regularized partial gradients of losses computed using an objective function for training the machine learning model.
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公开(公告)号:US20210326757A1
公开(公告)日:2021-10-21
申请号:US17227851
申请日:2021-04-12
Applicant: Google LLC
Inventor: Ankit Singh Rawat , Xinnan Yu , Aditya Krishna Menon , Sanjiv Kumar
Abstract: Generally, the present disclosure is directed to systems and methods that perform spreadout regularization to enable learning of a multi-class classification model in the federated setting, where each user has access to the positive data associated with only a limited number of classes (e.g., a single class). Examples of such settings include decentralized training of face recognition models or speaker identification models, where in addition to the user specific facial images and voice samples, the class embeddings for the users also constitute sensitive information that cannot be shared with other users.
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公开(公告)号:US20210319339A1
公开(公告)日:2021-10-14
申请号:US17227817
申请日:2021-04-12
Applicant: Google LLC
Inventor: Ankit Singh Rawat , Manzil Zaheer , Aditya Krishna Menon , Sanjiv Kumar , Melanie Weber
Abstract: Generally, the present disclosure provides systems and methods for performing machine learning in hyperbolic space. Specifically, techniques are provided which enable the learning of a classifier (e.g., large-margin classifier) for data defined within a hyperbolic space (e.g., which may be particularly beneficial for data that possesses a hierarchical structure).
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