Google llc (20240211764). COMPOUND MODEL SCALING FOR NEURAL NETWORKS simplified abstract
Contents
COMPOUND MODEL SCALING FOR NEURAL NETWORKS
Organization Name
Inventor(s)
Mingxing Tan of Newark CA (US)
Quoc V. Le of Sunnyvale CA (US)
COMPOUND MODEL SCALING FOR NEURAL NETWORKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240211764 titled 'COMPOUND MODEL SCALING FOR NEURAL NETWORKS
- Simplified Explanation:**
The abstract describes a method for determining the final architecture of a neural network for a specific machine learning task. This involves scaling the baseline architecture by assigning extra computational resources based on coefficients.
- Key Features and Innovation:**
- Method for determining final architecture of a neural network
- Utilizes baseline architecture with network width, depth, and resolution dimensions
- Involves a compound coefficient to control extra computational resources
- Search process to determine coefficients for scaling network width, depth, and resolution
- Generates final architecture based on coefficients to scale dimensions
- Potential Applications:**
This technology can be applied in various machine learning tasks where determining the optimal architecture of a neural network is crucial for performance.
- Problems Solved:**
This technology addresses the challenge of efficiently scaling the architecture of a neural network to improve performance without excessive computational resources.
- Benefits:**
- Optimizes neural network architecture for specific machine learning tasks
- Efficiently scales network dimensions for improved performance
- Utilizes computational resources effectively
- Commercial Applications:**
Optimizing neural network architectures can have significant implications in industries such as healthcare, finance, and technology where machine learning is utilized for various applications.
- Questions about the Technology:**
1. How does this method compare to existing techniques for determining neural network architectures? 2. What are the potential limitations of scaling the baseline architecture using coefficients in this method?
Original Abstract Submitted
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.
