- 专利标题: Removing nodes from machine-trained network based on introduction of probabilistic noise during training
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申请号: US16780842申请日: 2020-02-03
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公开(公告)号: US11900238B1公开(公告)日: 2024-02-13
- 发明人: Steven L. Teig , Eric A. Sather
- 申请人: Perceive Corporation
- 申请人地址: US CA San Jose
- 专利权人: PERCEIVE CORPORATION
- 当前专利权人: PERCEIVE CORPORATION
- 当前专利权人地址: US CA San Jose
- 代理机构: ADELI LLP
- 主分类号: G06N3/048
- IPC分类号: G06N3/048 ; G06N20/00 ; G06N3/084 ; G06F17/18 ; G06N3/047
摘要:
Some embodiments provide a method for reducing complexity of a machine-trained (MT) network that receives input data and computes output data for each input data. The MT network includes multiple computation nodes that (i) generate output values and (ii) use output values of other computation nodes as input values. During training of the MT network, the method introduces probabilistic noise to the output values of a set of the computation nodes. the method determines a subset of the computation nodes for which the introduction of the probabilistic noise to the output value does not affect the computed output data for the network. The method removes the subset of computation nodes from the trained MT network.
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