Deepmind technologies limited (20240242091). ENHANCING POPULATION-BASED TRAINING OF NEURAL NETWORKS simplified abstract
Contents
- 1 ENHANCING POPULATION-BASED TRAINING OF NEURAL NETWORKS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ENHANCING POPULATION-BASED TRAINING OF NEURAL NETWORKS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Questions about Neural Network Training
- 1.11 Original Abstract Submitted
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
Simplified Explanation
The patent application describes methods and systems for training a neural network to perform a task by evaluating candidate neural networks and updating hyperparameters based on fitness metrics.
Key Features and Innovation
- Training multiple candidate neural networks
- Evaluating each candidate using fitness functions
- Updating hyperparameters based on fitness metrics
- Selecting the best candidate neural network for the task
Potential Applications
This technology can be applied in various fields such as image recognition, natural language processing, and autonomous driving.
Problems Solved
This technology addresses the challenge of optimizing neural networks for specific tasks by efficiently evaluating and updating network parameters.
Benefits
- Improved performance of neural networks
- Faster training process
- Enhanced accuracy in task performance
Commercial Applications
- Image recognition software
- Speech recognition systems
- Autonomous vehicles technology
Questions about Neural Network Training
How does this technology improve the efficiency of training neural networks?
This technology streamlines the process by evaluating multiple candidate networks and updating hyperparameters based on fitness metrics, leading to improved performance.
What are the potential drawbacks of using this method for training neural networks?
One potential drawback could be the computational resources required to train and evaluate multiple candidate networks simultaneously.
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.
