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公开(公告)号：US11651286B2
公开(公告)日：2023-05-16
申请号：US17726910
申请日：2022-04-22
发明人： Andrew Feng , Erik Ordentlich , Lee Yang , Peter Cnudde
摘要： The present teaching relates to estimating one or more parameters on a system including a plurality of nodes. In one example, the system comprises: one or more learner nodes, each of which is configured for generating information related to a group of words for estimating the one or more parameters associated with a machine learning model; and a plurality of server nodes, each of which is configured for obtaining a plurality of sub-vectors each of which is a portion of a vector that represents a word in the group of words, updating the sub-vectors based at least partially on the information to generate a plurality of updated sub-vectors, and estimating at least one of the one or more parameters associated with the machine learning model based on the plurality of updated sub-vectors.
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公开(公告)号：US20220245525A1
公开(公告)日：2022-08-04
申请号：US17726910
申请日：2022-04-22
发明人： Andrew Feng , Erik Ordentlich , Lee Yang , Peter Cnudde
摘要： The present teaching relates to estimating one or more parameters on a system including a plurality of nodes. In one example, the system comprises: one or more learner nodes, each of which is configured for generating information related to a group of words for estimating the one or more parameters associated with a machine learning model; and a plurality of server nodes, each of which is configured for obtaining a plurality of sub-vectors each of which is a portion of a vector that represents a word in the group of words, updating the sub-vectors based at least partially on the information to generate a plurality of updated sub-vectors, and estimating at least one of the one or more parameters associated with the machine learning model based on the plurality of updated sub-vectors.
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公开(公告)号：US11334819B2
公开(公告)日：2022-05-17
申请号：US17005592
申请日：2020-08-28
发明人： Andrew Feng , Erik Ordentlich , Lee Yang , Peter Cnudde
摘要： The present teaching relates to estimating one or more parameters on a system including a plurality of nodes. In one example, the system comprises: one or more learner nodes, each of which is configured for generating information related to a group of words for estimating the one or more parameters associated with a machine learning model; and a plurality of server nodes, each of which is configured for obtaining a plurality of sub-vectors each of which is a portion of a vector that represents a word in the group of words, updating the sub-vectors based at least partially on the information to generate a plurality of updated sub-vectors, and estimating at least one of the one or more parameters associated with the machine learning model based on the plurality of updated sub-vectors.
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