公开(公告)号：US20240362453A1

公开(公告)日：2024-10-31

申请号：US18766038

申请日：2024-07-08

申请人： Google LLC

发明人： Anmol Gulati ,  Weikeng Qin ,  Zhengdong Zhang ,  Ruoming Pang ,  Niki Parmar ,  Jiahui Yu ,  Wei Han ,  Chung-Cheng Chiu ,  Yu Zhang ,  Yonghui Wu ,  Shibo Wang

IPC分类号： G06N3/04 ,  G06N20/00 ,  G10L15/16

CPC分类号： G06N3/04 ,  G06N20/00 ,  G10L15/16

摘要： Systems and methods can utilize a conformer model to process a data set for various data processing tasks, including, but not limited to, speech recognition, sound separation, protein synthesis determination, video or other image set analysis, and natural language processing. The conformer model can use feed-forward blocks, a self-attention block, and a convolution block to process data to learn global interactions and relative-offset-based local correlations of the input data.

公开(公告)号：US12079703B2

公开(公告)日：2024-09-03

申请号：US17139525

申请日：2020-12-31

申请人： Google LLC

发明人： Anmol Gulati ,  Ruoming Pang ,  Niki Parmar ,  Jiahui Yu ,  Wei Han ,  Chung-Cheng Chiu ,  Yu Zhang ,  Yonghui Wu ,  Shibo Wang ,  Weikeng Qin ,  Zhengdong Zhang

IPC分类号： G06N3/04 ,  G06N20/00 ,  G10L15/16

CPC分类号： G06N3/04 ,  G06N20/00 ,  G10L15/16

摘要： Systems and methods can utilize a conformer model to process a data set for various data processing tasks, including, but not limited to, speech recognition, sound separation, protein synthesis determination, video or other image set analysis, and natural language processing. The conformer model can use feed-forward blocks, a self-attention block, and a convolution block to process data to learn global interactions and relative-offset-based local correlations of the input data.

公开(公告)号：US20220207321A1

公开(公告)日：2022-06-30

申请号：US17139525

申请日：2020-12-31

申请人： Google LLC

发明人： Anmol Gulati ,  Ruoming Pang ,  Niki Parmar ,  Jiahui Yu ,  Wei Han ,  Chung-Cheng Chiu ,  Yu Zhang ,  Yonghui Wu ,  Shibo Wang ,  Weikeng Qin ,  Zhengdong Zhang

IPC分类号： G06N3/04 ,  G10L15/16 ,  G06N20/00

摘要： Systems and methods can utilize a conformer model to process a data set for various data processing tasks, including, but not limited to, speech recognition, sound separation, protein synthesis determination, video or other image set analysis, and natural language processing. The conformer model can use feed-forward blocks, a self-attention block, and a convolution block to process data to learn global interactions and relative-offset-based local correlations of the input data.