18519566. METHOD FOR TRAINING AN ARTIFICIAL NEURAL NETWORK simplified abstract (Robert Bosch GmbH)
Contents
- 1 METHOD FOR TRAINING AN ARTIFICIAL NEURAL NETWORK
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD FOR TRAINING AN ARTIFICIAL NEURAL NETWORK - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 How does this method compare to existing techniques for training artificial neural networks?
- 1.11 What are the specific types of resources considered in the cost function for training the artificial neural network?
- 1.12 Original Abstract Submitted
METHOD FOR TRAINING AN ARTIFICIAL NEURAL NETWORK
Organization Name
Inventor(s)
METHOD FOR TRAINING AN ARTIFICIAL NEURAL NETWORK - A simplified explanation of the abstract
This abstract first appeared for US patent application 18519566 titled 'METHOD FOR TRAINING AN ARTIFICIAL NEURAL NETWORK
Simplified Explanation
The abstract describes a method for training an artificial neural network by considering available resources in addition to the learning task.
- Providing training data for the artificial neural network
- Detecting specifications regarding available resources
- Ascertaining a cost function that considers the learning task and available resources
- Training the artificial neural network using the cost function
Potential Applications
This technology could be applied in various fields such as healthcare, finance, and autonomous systems where resource optimization is crucial for training artificial neural networks.
Problems Solved
This method addresses the challenge of efficiently training artificial neural networks by taking into account available resources, leading to improved performance and cost-effectiveness.
Benefits
The benefits of this technology include enhanced training efficiency, optimized resource utilization, and improved overall performance of artificial neural networks.
Potential Commercial Applications
The potential commercial applications of this technology include developing advanced AI systems, optimizing resource-intensive processes, and enhancing the performance of machine learning models in various industries.
Possible Prior Art
One possible prior art could be the use of cost functions in training neural networks to optimize performance and resource utilization. Another could be the incorporation of resource constraints in machine learning algorithms to improve efficiency.
Unanswered Questions
How does this method compare to existing techniques for training artificial neural networks?
This article does not provide a direct comparison to existing techniques for training artificial neural networks. It would be beneficial to understand the specific advantages and limitations of this method compared to traditional approaches.
What are the specific types of resources considered in the cost function for training the artificial neural network?
The abstract mentions considering available resources in training the artificial neural network, but it does not specify the types of resources included in the cost function. Understanding the specific resources considered could provide insights into the applicability of this method in different scenarios.
Original Abstract Submitted
A method for training an artificial neural network. The method includes the following steps: providing training data for training the artificial neural network; detecting at least one specification regarding available resources; ascertaining a cost function that, in addition to an actual learning task, also takes into account the at least one specification regarding available resources; and training the artificial neural network on the basis of the provided training data using the ascertained cost function.
