DeepMind Technologies Limited (20240242091). ENHANCING POPULATION-BASED TRAINING OF NEURAL NETWORKS simplified abstract
Contents
ENHANCING POPULATION-BASED TRAINING OF NEURAL NETWORKS
Organization Name
Inventor(s)
Valentin Clement Dalibard of London (GB)
Maxwell Elliot Jaderberg of London (GB)
ENHANCING POPULATION-BASED TRAINING OF NEURAL NETWORKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240242091 titled 'ENHANCING POPULATION-BASED TRAINING OF NEURAL NETWORKS
The abstract describes a method and system for training a neural network to perform a task by maintaining data on candidate neural networks and partitions, training and evaluating the networks, and updating hyperparameters based on fitness metrics.
- The system maintains data on candidate neural networks and partitions.
- It trains each candidate neural network and evaluates them using fitness functions.
- Hyperparameters for the networks are updated based on fitness metrics.
- The system selects the values of network parameters for the best candidate neural network.
Potential Applications: - This technology can be applied in various fields such as image recognition, natural language processing, and autonomous driving. - It can be used in healthcare for medical image analysis and diagnosis.
Problems Solved: - Helps in optimizing neural networks for better performance. - Streamlines the training process and improves efficiency.
Benefits: - Enhances the accuracy and efficiency of neural networks. - Allows for faster training and better performance in various tasks.
Commercial Applications: Title: "Optimized Neural Network Training System for Enhanced Performance" This technology can be utilized in industries such as tech, healthcare, finance, and manufacturing for improving processes, decision-making, and automation.
Questions about the technology: 1. How does this system improve the training process of neural networks? 2. What are the key factors considered in evaluating candidate neural networks?
Frequently Updated Research: Stay updated on advancements in neural network training methods and hyperparameter optimization techniques to enhance the performance of AI systems.
Original Abstract Submitted
methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network for performing a task. the system maintains data specifying (i) a plurality of candidate neural networks and (ii) a partitioning of the plurality of candidate neural networks into a plurality of partitions. the system repeatedly performs operations, including: training each of the candidate neural networks; evaluating each candidate neural network using a respective fitness function for the partition; and for each partition, updating the respective values of the one or more hyperparameters for at least one of the candidate neural networks in the partition based on the respective fitness metrics of the candidate neural networks in the partition. after repeatedly performing the operations, the system selects, from the maintained data, the respective values of the network parameters of one of the candidate neural networks.
