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公开(公告)号:US12106214B2
公开(公告)日:2024-10-01
申请号:US17968085
申请日:2022-10-18
发明人: Stefan Braun , Daniel Neil , Enea Ceolini , Jithendar Anumula , Shih-Chii Liu
IPC分类号: G06N3/08 , G06F18/2413 , G06F18/25 , G06N3/04 , G06N3/0442 , G06N3/0455 , G06N3/0464 , G06N3/084 , G06V10/44 , G06V10/46 , G06V10/764 , G06V10/80 , G06V10/82 , G06V20/10 , G10L15/16 , G10L15/20 , G10L15/24
CPC分类号: G06N3/08 , G06F18/2413 , G06F18/256 , G06N3/04 , G06N3/0442 , G06N3/0455 , G06N3/0464 , G06N3/084 , G06V10/454 , G06V10/462 , G06V10/764 , G06V10/806 , G06V10/811 , G06V20/10 , G10L15/16 , G06V10/82 , G10L15/20 , G10L15/24
摘要: A sensor transformation attention network (STAN) model including sensors configured to collect input signals, attention modules configured to calculate attention scores of feature vectors corresponding to the input signals, a merge module configured to calculate attention values of the attention scores, and generate a merged transformation vector based on the attention values and the feature vectors, and a task-specific module configured to classify the merged transformation vector is provided.
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公开(公告)号:US20230045790A1
公开(公告)日:2023-02-16
申请号:US17968085
申请日:2022-10-18
发明人: Stefan BRAUN , Daniel Neil , Enea Ceolini , Jithendar Anumula , Shih-Chii Lui
IPC分类号: G06N3/08 , G06K9/62 , G06V20/10 , G06V10/764 , G06V10/44 , G06V10/80 , G10L15/16 , G06N3/04 , G10L15/24 , G06V10/82
摘要: A sensor transformation attention network (STAN) model including sensors configured to collect input signals, attention modules configured to calculate attention scores of feature vectors corresponding to the input signals, a merge module configured to calculate attention values of the attention scores, and generate a merged transformation vector based on the attention values and the feature vectors, and a task-specific module configured to classify the merged transformation vector is provided.
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公开(公告)号:US11501154B2
公开(公告)日:2022-11-15
申请号:US15911969
申请日:2018-03-05
发明人: Stefan Braun , Daniel Neil , Enea Ceolini , Jithendar Anumula , Shih-Chii Liu
IPC分类号: G06N3/08 , G06N3/04 , G10L15/16 , G06K9/62 , G06V10/44 , G06V20/10 , G06V10/80 , G06V10/764 , G10L15/24 , G06V10/82
摘要: A sensor transformation attention network (STAN) model including sensors, attention modules, a merge module and a task-specific module is provided. The attention modules calculate attention scores of feature vectors corresponding to the input signals collected by the sensors. The merge module calculates attention values of the attention scores, and generates a merged transformation vector based on the attention values and the feature vectors. The task-specific module classifies the merged transformation vector.
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