Selecting a neural network architecture for a supervised machine learning problem

A machine learning and neural network technology, applied in neural architecture, biological neural network models, neural learning methods, etc.
CN112470171APending Publication Date: 2021-03-09MICROSOFT TECH LICENSING LLC
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
MICROSOFT TECH LICENSING LLC
Publication Date
2021-03-09

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Abstract

Systems and methods for selecting a neural network for a machine learning problem are disclosed. A method includes accessing an input matrix. The method includes accessing a machine learning problem space associated with a machine learning problem and multiple untrained candidate neural networks for solving the machine learning problem. The method includes computing, for each untrained candidate neural network, at least one expressivity measure capturing an expressivity of the candidate neural network with respect to the machine learning problem. The method includes computing, for each untrained candidate neural network, at least one trainability measure capturing a trainability of the candidate neural network with respect to the machine learning problem. The method includes selecting, based on the at least one expressivity measure and the at least one trainability measure, at least one candidate neural network for solving the machine learning problem.
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Description

Background technique

[0001] Many different types of neural network architectures are known (eg, convolutional neural networks, feed-forward neural networks, etc.). Choosing a neural network architecture (and sub-architectures within a given architecture type) to solve a given machine learning problem can be challenging. Description of drawings

[0002] In the figures of the drawings, some embodiments of the present technology are shown by way of example and not limitation.

[0003] figure 1 Illustrated is an example system in which selecting a neural network architecture for solving a machine learning problem may be implemented in accordance with some embodiments.

[0004] figure 2 A flowchart illustrating an example method for selecting a neural network architecture for solving a machine learning problem according to some embodiments.

[0005] image 3 A flowchart illustrating an example method for reducing error rates according to some embodiments is shown.

[0006] ...

Claims

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