Invention Grant
- Patent Title: Deep learning-based techniques for pre-training deep convolutional neural networks
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Application No.: US16407149Application Date: 2019-05-08
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Publication No.: US10540591B2Publication Date: 2020-01-21
- Inventor: Hong Gao , Kai-How Farh , Samskruthi Reddy Padigepati
- Applicant: Illumina, Inc.
- Applicant Address: US CA San Diego
- Assignee: Illumina, Inc.
- Current Assignee: Illumina, Inc.
- Current Assignee Address: US CA San Diego
- Agency: Haynes Beffel & Wolfeld, LLP
- Agent Ernest J. Beffel, Jr.; Paul A. Durdik
- Main IPC: G06K9/62
- IPC: G06K9/62 ; G06N3/12 ; G06N3/08

Abstract:
The technology disclosed includes systems and methods to reduce overfitting of neural network-implemented models that process sequences of amino acids and accompanying position frequency matrices. The system generates supplemental training example sequence pairs, labelled benign, that include a start location, through a target amino acid location, to an end location. A supplemental sequence pair supplements a pathogenic or benign missense training example sequence pair. It has identical amino acids in a reference and an alternate sequence of amino acids. The system includes logic to input with each supplemental sequence pair a supplemental training position frequency matrix (PFM) that is identical to the PFM of the benign or pathogenic missense at the matching start and end location. The system includes logic to attenuate the training influence of the training PFMs during training the neural network-implemented model by including supplemental training example PFMs in the training data.
Public/Granted literature
- US20190266493A1 Deep Learning-Based Techniques for Pre-Training Deep Convolutional Neural Networks Public/Granted day:2019-08-29
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