- 专利标题: Machine-learning models based on non-local neural networks
-
申请号: US16192649申请日: 2018-11-15
-
公开(公告)号: US11562243B2公开(公告)日: 2023-01-24
- 发明人: Kaiming He , Ross Girshick , Xiaolong Wang
- 申请人: Meta Platforms, Inc.
- 申请人地址: US CA Menlo Park
- 专利权人: Meta Platforms, Inc.
- 当前专利权人: Meta Platforms, Inc.
- 当前专利权人地址: US CA Menlo Park
- 代理机构: Baker Botts L.L.P.
- 主分类号: G06N3/08
- IPC分类号: G06N3/08 ; G06F17/15 ; G06N3/04 ; G06F16/903
摘要:
In one embodiment, a method includes training a baseline machine-learning model based on a neural network comprising a plurality of stages, wherein each stage comprises a plurality of neural blocks, accessing a plurality of training samples comprising a plurality of content objects, respectively, determining one or more non-local operations, wherein each non-local operation is based on one or more pairwise functions and one or more unary functions, generating one or more non-local blocks based on the plurality of training samples and the one or more non-local operations, determining a stage from the plurality of stages of the neural network, and training a non-local machine-learning model by inserting each of the one or more non-local blocks in between at least two of the plurality of neural blocks in the determined stage of the neural network.
公开/授权文献
信息查询