Invention Grant
- Patent Title: Training of artificial neural networks using safe mutations based on output gradients
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Application No.: US16220541Application Date: 2018-12-14
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Publication No.: US10699195B2Publication Date: 2020-06-30
- Inventor: Joel Anthony Lehman , Kenneth Owen Stanley , Jeffrey Michael Clune
- Applicant: Uber Technologies, Inc.
- Applicant Address: US CA San Francisco
- Assignee: Uber Technologies, Inc.
- Current Assignee: Uber Technologies, Inc.
- Current Assignee Address: US CA San Francisco
- Agency: Fenwick & West LLP
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06K9/62 ; G06N3/04

Abstract:
Systems and methods are disclosed herein for ensuring a safe mutation of a neural network. A processor determines a threshold value representing a limit on an amount of divergence of response for the neural network. The processor identifies a set of weights for the neural network, the set of weights beginning as an initial set of weights. The processor trains the neural network by repeating steps including determining a safe mutation representing a perturbation that results in a response of the neural network that is within the threshold divergence, and modifying the set of weights of the neural network in accordance with the safe mutation.
Public/Granted literature
- US20190188573A1 TRAINING OF ARTIFICIAL NEURAL NETWORKS USING SAFE MUTATIONS BASED ON OUTPUT GRADIENTS Public/Granted day:2019-06-20
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