NEURAL NETWORK ARCHITECTURE SEARCH OVER COMPLEX BLOCK ARCHITECTURES

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

Neural architecture search for machine learning models:

   - Involves generating candidate neural networks with different layer configurations
   - Training and evaluating these networks to determine performance scores
   - Selecting a final neural network based on the performance scores

Potential Applications

The technology can be applied in various fields such as image recognition, natural language processing, and autonomous driving.

Problems Solved

This technology helps in automating the process of designing neural networks, saving time and resources compared to manual trial-and-error methods.

Benefits

The benefits of this technology include improved efficiency in developing machine learning models, increased accuracy in model performance, and the ability to explore a wider range of network architectures.

Potential Commercial Applications

The technology can be used in industries such as healthcare for medical image analysis, finance for fraud detection, and e-commerce for personalized recommendations.

Possible Prior Art

Prior art in neural architecture search includes methods like reinforcement learning-based approaches, evolutionary algorithms, and random search strategies.

Unanswered Questions

How does this technology compare to existing neural architecture search methods?

This technology utilizes a systematic approach to explore different layer configurations, potentially leading to more optimized neural networks. However, the efficiency and effectiveness of this method compared to other approaches remain to be seen through further research and experimentation.

What are the computational requirements for implementing this technology?

The computational resources needed to perform neural architecture search using this method, such as training time, memory usage, and hardware specifications, are important factors to consider for practical applications. Further analysis and optimization may be necessary to address these requirements.

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.

Google llc (20240112027). NEURAL NETWORK ARCHITECTURE SEARCH OVER COMPLEX BLOCK ARCHITECTURES simplified abstract

Contents

NEURAL NETWORK ARCHITECTURE SEARCH OVER COMPLEX BLOCK ARCHITECTURES

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