Invention Grant
- Patent Title: Training artificial neural networks based on synaptic connectivity graphs
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Application No.: US16731331Application Date: 2019-12-31
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Publication No.: US11625611B2Publication Date: 2023-04-11
- Inventor: Sarah Ann Laszlo , Philip Edwin Watson
- Applicant: X Development LLC
- Applicant Address: US CA Mountain View
- Assignee: X Development LLC
- Current Assignee: X Development LLC
- Current Assignee Address: US CA Mountain View
- Agency: Fish & Richardson P.C.
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06N20/20 ; G06K9/62 ; G06N3/04 ; G06N3/086 ; G06N3/084

Abstract:
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a student neural network. In one aspect, there is provided a method comprising: processing a training input using the student neural network to generate an output for the training input; processing the student neural network output using a discriminative neural network to generate a discriminative score for the student neural network output, wherein the discriminative score characterizes a prediction for whether the network input was generated using: (i) the student neural network, or (ii) a brain emulation neural network; and adjusting current values of the student neural network parameters using gradients of an objective function that depends on the discriminative score for the student neural network output.
Public/Granted literature
- US20210201158A1 TRAINING ARTIFICIAL NEURAL NETWORKS BASED ON SYNAPTIC CONNECTIVITY GRAPHS Public/Granted day:2021-07-01
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