Deepmind technologies limited (20240281714). BLACK-BOX OPTIMIZATION USING NEURAL NETWORKS simplified abstract
Contents
BLACK-BOX OPTIMIZATION USING NEURAL NETWORKS
Organization Name
Inventor(s)
Joao Ferdinando Gomes De Freitas of London (GB)
BLACK-BOX OPTIMIZATION USING NEURAL NETWORKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240281714 titled 'BLACK-BOX OPTIMIZATION USING NEURAL NETWORKS
The patent application discusses methods and systems for determining an optimized setting for process parameters in a machine learning training process.
- Processing a current network input using a recurrent neural network with initial network parameters to obtain a current network output.
- Evaluating the performance of the training process with an updated setting defined by the current network output.
- Generating a new network input that includes the updated setting and the performance measure of the training process.
Potential Applications: - Optimization of machine learning training processes - Improving the efficiency and accuracy of neural networks
Problems Solved: - Finding the optimal settings for machine learning training processes - Enhancing the performance of neural networks
Benefits: - Increased efficiency in machine learning training - Improved accuracy of neural networks
Commercial Applications: - This technology can be used in various industries such as healthcare, finance, and marketing for data analysis and predictive modeling.
Questions about the technology: 1. How does this technology improve the performance of machine learning training processes? 2. What are the potential implications of using recurrent neural networks in optimizing process parameters?
Frequently Updated Research: - Stay updated on the latest advancements in machine learning optimization techniques and neural network training processes.
Original Abstract Submitted
methods and systems for determining an optimized setting for one or more process parameters of a machine learning training process. one of the methods includes processing a current network input using a recurrent neural network in accordance with first values of the network parameters to obtain a current network output, obtaining a measure of the performance of the machine learning training process with an updated setting defined by the current network output, and generating a new network input that comprises (i) the updated setting defined by the current network output and (ii) the measure of the performance of the training process with the updated setting defined by the current network output.
