Deepmind technologies limited (20240346310). POPULATION BASED TRAINING OF NEURAL NETWORKS simplified abstract
Contents
POPULATION BASED TRAINING OF NEURAL NETWORKS
Organization Name
Inventor(s)
Maxwell Elliot Jaderberg of London (GB)
Wojciech Czarnecki of London (GB)
Timothy Frederick Goldie Green of London (GB)
Valentin Clement Dalibard of London (GB)
POPULATION BASED TRAINING OF NEURAL NETWORKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240346310 titled 'POPULATION BASED TRAINING OF NEURAL NETWORKS
Simplified Explanation: The patent application describes methods, systems, and apparatus for training a neural network using an iterative process with multiple hyperparameters.
- Neural network trained for specific tasks
- Iterative training process with hyperparameters
- Maintaining candidate neural networks with parameters and quality measures
- Additional training operations for candidate networks
Key Features and Innovation:
- Training neural networks for specific tasks
- Iterative process with hyperparameters
- Maintaining candidate networks with parameters and quality measures
- Additional training operations for candidate networks
Potential Applications:
- Artificial intelligence
- Machine learning
- Data analysis
- Pattern recognition
Problems Solved:
- Efficient training of neural networks
- Optimization of hyperparameters
- Improving performance on specific tasks
Benefits:
- Enhanced neural network training
- Improved task performance
- Streamlined training process
Commercial Applications: Artificial Intelligence Training Systems for Various Industries
Prior Art: Research on Neural Network Training and Hyperparameter Optimization
Frequently Updated Research: Latest advancements in Neural Network Training Techniques
Questions about Neural Network Training: 1. How do hyperparameters impact neural network training? 2. What are the key challenges in optimizing neural network performance?
Original Abstract Submitted
methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network. a method includes: training a neural network having a plurality of network parameters to perform a particular neural network task and to determine trained values of the network parameters using an iterative training process having a plurality of hyperparameters, the method comprising: maintaining a plurality of candidate neural networks and, for each of the candidate neural networks, data specifying: (i) respective values of the network parameters for the candidate neural network, (ii) respective values of the hyperparameters for the candidate neural network, and (iii) a quality measure that measures a performance of the candidate neural network on the particular neural network task; and for each of the plurality of candidate neural networks, repeatedly performing additional training operations.
