18477546. NEURAL NETWORK ARCHITECTURE SEARCH OVER COMPLEX BLOCK ARCHITECTURES simplified abstract (GOOGLE LLC)

NEURAL NETWORK ARCHITECTURE SEARCH OVER COMPLEX BLOCK ARCHITECTURES

Organization Name

GOOGLE LLC

Inventor(s)

Yanqi Zhou of Sunnyvale CA (US)

Yanping Huang of Mountain View CA (US)

Yifeng Lu of Palo Alto CA (US)

Andrew M. Dai of San Francisco CA (US)

Siamak Shakeri of New York NY (US)

Zhifeng Chen of Sunnyvale CA (US)

James Laudon of Madison WI (US)

Quoc V. Le of Sunnyvale CA (US)

Da Huang of Santa Clara CA (US)

Nan Du of San Jose CA (US)

David Richard So of Brooklyn NY (US)

Daiyi Peng of Cupertino CA (US)

Yingwei Cui of Los Altos CA (US)

Jeffrey Adgate Dean of Palo Alto CA (US)

Chang Lan of Kirkland WA (US)

NEURAL NETWORK ARCHITECTURE SEARCH OVER COMPLEX BLOCK ARCHITECTURES - A simplified explanation of the abstract

This abstract first appeared for US patent application 18477546 titled 'NEURAL NETWORK ARCHITECTURE SEARCH OVER COMPLEX BLOCK ARCHITECTURES

Simplified Explanation

The patent application describes methods, systems, and apparatus for performing neural architecture search for machine learning models. This involves generating candidate neural networks composed of layer blocks with different types of layers, training and evaluating these networks, and selecting a final neural network based on performance scores.

• Simplified Explanation:

 - Developing and optimizing neural networks for machine learning tasks through a systematic search process.

• Potential Applications:

 - Improving the efficiency and accuracy of machine learning models.
 - Automating the process of designing neural networks for various applications.

• Problems Solved:

 - Streamlining the development of neural networks.
 - Enhancing the performance of machine learning algorithms.

• Benefits:

 - Faster and more effective creation of neural networks.
 - Increased accuracy and efficiency in machine learning tasks.

• Potential Commercial Applications:

 - AI and machine learning software development.
 - Data analysis and predictive modeling industries.

• Possible Prior Art:

 - Previous methods of manually designing neural network architectures.
 - Existing automated machine learning tools for model optimization.

1. Unanswered Questions
   1. How does this technology compare to existing neural architecture search methods?

This article does not provide a direct comparison with other neural architecture search techniques, leaving the reader to wonder about the specific advantages and limitations of this approach.

1. 1. What are the potential limitations or challenges of implementing this technology in real-world applications?

The article does not address the practical considerations or obstacles that may arise when applying this technology in different industries or scenarios, leaving room for speculation on its feasibility and scalability.

Original Abstract Submitted

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing neural architecture search for machine learning models. In one aspect, a method comprises receiving training data for a machine learning, generating a plurality of candidate neural networks for performing the machine learning task, wherein each candidate neural network comprises a plurality of instances of a layer block composed of a plurality of layers, for each candidate neural network, selecting a respective type for each of the plurality of layers from a set of layer types that comprises, training the candidate neural network and evaluating performance scores for the trained candidate neural networks as applied to the machine learning task, and determining a final neural network for performing the machine learning task based at least on the performance scores for the candidate neural networks.