Invention Grant
- Patent Title: Compound model scaling for neural networks
-
Application No.: US17144450Application Date: 2021-01-08
-
Publication No.: US11893491B2Publication Date: 2024-02-06
- Inventor: Mingxing Tan , Quoc V. Le
- Applicant: Google LLC
- Applicant Address: US CA Mountain View
- Assignee: Google LLC
- Current Assignee: Google LLC
- Current Assignee Address: US CA Mountain View
- Agency: Fish & Richardson P.C.
- Main IPC: G06N3/04
- IPC: G06N3/04 ; G06N3/082

Abstract:
A method for determining a final architecture for a neural network to perform a particular machine learning task is described. The method includes receiving a baseline architecture for the neural network, wherein the baseline architecture has a network width dimension, a network depth dimension, and a resolution dimension; receiving data defining a compound coefficient that controls extra computational resources used for scaling the baseline architecture; performing a search to determine a baseline width, depth and resolution coefficient that specify how to assign the extra computational resources to the network width, depth and resolution dimensions of the baseline architecture, respectively; determining a width, depth and resolution coefficient based on the baseline width, depth, and resolution coefficient and the compound coefficient; and generating the final architecture that scales the network width, network depth, and resolution dimensions of the baseline architecture based on the corresponding width, depth, and resolution coefficients.
Public/Granted literature
- US20210133578A1 COMPOUND MODEL SCALING FOR NEURAL NETWORKS Public/Granted day:2021-05-06
Information query