20240013026. METHOD FOR ASCERTAINING AN OPTIMAL ARCHITECTURE OF AN ARTIFICIAL NEURAL NETWORK simplified abstract (Robert Bosch GmbH)
METHOD FOR ASCERTAINING AN OPTIMAL ARCHITECTURE OF AN ARTIFICIAL NEURAL NETWORK
Organization Name
Inventor(s)
Jan Hendrik Metzen of Boeblingen (DE)
METHOD FOR ASCERTAINING AN OPTIMAL ARCHITECTURE OF AN ARTIFICIAL NEURAL NETWORK - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240013026 titled 'METHOD FOR ASCERTAINING AN OPTIMAL ARCHITECTURE OF AN ARTIFICIAL NEURAL NETWORK
Simplified Explanation
The abstract of the patent application describes a method for determining the optimal architecture of an artificial neural network. The method involves repeatedly finding a path from the initial node to a terminal node based on a defined strategy. A reward is determined for each path, and a cost function is calculated based on the reward and the flows associated with the edges along the path. The flows are updated based on the cost function until a termination criterion is met, indicating the optimal architecture.
- The method repeatedly finds a path from the initial node to a terminal node in an artificial neural network.
- A reward is determined for each path.
- A cost function is calculated based on the reward and the flows associated with the edges along the path.
- The flows associated with the edges along the path are updated based on the cost function.
- The process continues until a termination criterion is met, indicating the optimal architecture.
Potential applications of this technology:
- Artificial intelligence: This method can be used to optimize the architecture of artificial neural networks used in various AI applications, such as image recognition, natural language processing, and autonomous systems.
- Data analysis: The method can help in determining the optimal architecture of neural networks used for data analysis tasks, such as predictive modeling, pattern recognition, and anomaly detection.
- Robotics: Optimizing the architecture of neural networks in robotics applications can improve the performance and efficiency of robotic systems, enabling them to perform complex tasks more effectively.
Problems solved by this technology:
- Architecture optimization: The method solves the problem of determining the most effective architecture for an artificial neural network, which can greatly impact its performance and accuracy.
- Resource utilization: By optimizing the architecture, the method helps in utilizing computational resources more efficiently, reducing the computational burden and improving overall system performance.
Benefits of this technology:
- Improved performance: The optimized architecture of the neural network leads to improved accuracy and performance in various applications, resulting in better decision-making and outcomes.
- Efficient resource utilization: By finding the optimal architecture, the method ensures that computational resources are utilized efficiently, reducing costs and improving scalability.
- Time-saving: The automated process of determining the optimal architecture eliminates the need for manual trial and error, saving time and effort in designing neural networks.
Original Abstract Submitted
a method for ascertaining an optimal architecture of an artificial neural network. the method includes: ascertaining the optimal architecture of the artificial neural network by repeatedly ascertaining a trajectory from the initial node to a terminal node based on the defined strategy, determining a reward for the ascertained trajectory, determining a cost function for the ascertained trajectory based on the ascertained reward for the trajectory and the flows associated with the edges along the trajectory, and respectively updating the flows associated with the edges along the trajectory, based on the cost function until an ascertained trajectory fulfills a termination criterion for the architecture search, wherein the trajectory that fulfills the termination criterion represents the optimal architecture.
