Google llc (20240289619). GRADIENT-FREE STRUCTURED PRUNING OF NEURAL NETWORKS simplified abstract
Contents
GRADIENT-FREE STRUCTURED PRUNING OF NEURAL NETWORKS
Organization Name
Inventor(s)
Azade Nova of San Jose CA (US)
Dale Eric Schuurmans of Edmonton (CA)
GRADIENT-FREE STRUCTURED PRUNING OF NEURAL NETWORKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240289619 titled 'GRADIENT-FREE STRUCTURED PRUNING OF NEURAL NETWORKS
Simplified Explanation: The patent application describes methods, systems, and apparatus for performing a machine learning task on a network input to generate a network output. It involves obtaining data for an initial neural network, determining the representativeness measure and central tendency measure for filters, selecting a subset of filters, and generating a pruned neural network.
Key Features and Innovation:
- Utilizes an initial neural network for a machine learning task.
- Measures the representativeness and central tendency of filters.
- Selects a proper subset of filters based on importance scores.
- Generates a pruned neural network for improved efficiency.
Potential Applications: This technology can be applied in various fields such as image recognition, natural language processing, and predictive analytics.
Problems Solved: Addresses the challenge of optimizing neural networks by selecting the most important filters and reducing computational complexity.
Benefits:
- Enhances the efficiency of machine learning tasks.
- Improves the performance of neural networks.
- Reduces computational resources required for processing.
Commercial Applications: The technology can be utilized in industries such as healthcare for medical image analysis, finance for fraud detection, and e-commerce for personalized recommendations.
Prior Art: Researchers can explore prior studies on neural network pruning techniques and filter importance measures in machine learning.
Frequently Updated Research: Stay updated on advancements in neural network pruning algorithms and techniques for optimizing machine learning models.
Questions about Machine Learning Pruning: 1. How does neural network pruning improve computational efficiency? 2. What are the key factors considered when selecting filters for pruning?
Original Abstract Submitted
methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing a machine learning task on a network input to generate a network output. one of the methods includes: obtaining data specifying an initial neural network configured to perform a machine learning task; a representativeness measure for each of a plurality of filters; determining a central tendency measure for the plurality of filters based on processing a batch of network inputs using the initial neural network; determining a cumulative importance score for each of the plurality of filters; selecting a proper subset of the plurality of filters; and generating a pruned neural network configured to perform the machine learning task.
