Invention Grant
- Patent Title: Block-diagonal hessian-free optimization for recurrent and convolutional neural networks
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Application No.: US15983782Application Date: 2018-05-18
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Publication No.: US11386327B2Publication Date: 2022-07-12
- Inventor: Huishuai Zhang , Caiming Xiong
- Applicant: salesforce.com, inc.
- Applicant Address: US CA San Francisco
- Assignee: salesforce.com, inc.
- Current Assignee: salesforce.com, inc.
- Current Assignee Address: US CA San Francisco
- Agency: Haynes and Boone, LLP
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06N3/04 ; G10L15/16 ; G06F17/16 ; G06F17/11 ; G06N7/00 ; G06N5/00

Abstract:
Embodiments for training a neural network are provided. A neural network is divided into a first block and a second block, and the parameters in the first block and second block are trained in parallel. To train the parameters, a gradient from a gradient mini-batch included in training data is generated. A curvature-vector product from a curvature mini-batch included in the training data is also generated. The gradient and the curvature-vector product generate a conjugate gradient. The conjugate gradient is used to determine a change in parameters in the first block in parallel with a change in parameters in the second block. The curvature matrix in the curvature-vector product includes zero values when the terms correspond to parameters from different blocks.
Public/Granted literature
- US20180373987A1 BLOCK-DIAGONAL HESSIAN-FREE OPTIMIZATION FOR RECURRENT AND CONVOLUTIONAL NEURAL NETWORKS Public/Granted day:2018-12-27
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