- 专利标题: Neural network architectures employing interrelatedness
-
申请号: US16160540申请日: 2018-10-15
-
公开(公告)号: US10938840B2公开(公告)日: 2021-03-02
- 发明人: Jack Wilson Stokes, III , Rakshit Agrawal , Karthik Selvaraj , Adrian M. Marinescu
- 申请人: MICROSOFT TECHNOLOGY LICENSING, LLC
- 申请人地址: US WA Redmond
- 专利权人: MICROSOFT TECHNOLOGY LICENSING, LLC
- 当前专利权人: MICROSOFT TECHNOLOGY LICENSING, LLC
- 当前专利权人地址: US WA Redmond
- 代理机构: Shook, Hardy & Bacon L.L.P.
- 主分类号: G06F21/00
- IPC分类号: G06F21/00 ; H04L29/06 ; G06N3/04 ; G06N3/08
摘要:
Enhanced neural network architectures that enable the determination and employment of association-based or attention-based “interrelatedness” of various portions of the input data are provided. A method of employing an architecture includes receiving a first input data element, a second input element, and a third input element. A first interrelated metric that indicates a degree of interrelatedness between the first input data element and the second input data element is determined. A second interrelated metric is determined. The second interrelated metric indicates a degree of interrelatedness between the first input data element and the third input data element. An interrelated vector is generated based on the first interrelated metric and the second interrelated metric. The neural network is employed to generate an output vector that corresponds to the first input vector and is based on a combination of the first input vector and the interrelated vector.
信息查询