18473136. AUTOMATICALLY ASCERTAINING AN OPTIMAL ARCHITECTURE FOR A NEURAL NETWORK simplified abstract (Robert Bosch GmbH)
Contents
AUTOMATICALLY ASCERTAINING AN OPTIMAL ARCHITECTURE FOR A NEURAL NETWORK
Organization Name
Inventor(s)
Benedikt Sebastian Staffler of Tuebingen (DE)
David Stoeckel of Rutesheim (DE)
AUTOMATICALLY ASCERTAINING AN OPTIMAL ARCHITECTURE FOR A NEURAL NETWORK - A simplified explanation of the abstract
This abstract first appeared for US patent application 18473136 titled 'AUTOMATICALLY ASCERTAINING AN OPTIMAL ARCHITECTURE FOR A NEURAL NETWORK
Simplified Explanation
The computer-implemented method described in the abstract aims to find the optimal architecture for a neural network to solve a specific task while adhering to given boundary conditions and optimization goals. Here is a simplified explanation of the patent application:
- The method involves providing a graph representing possible architectures of nodes and edges, where nodes represent data and edges represent parameterized operations to be performed on the data.
- During the search phase, candidate architectures are generated based on known architectures, with slight variations according to a predetermined criterion.
- The candidate architectures are then evaluated using the specified boundary conditions and optimization goals.
Potential Applications: - This technology could be applied in various fields such as image recognition, natural language processing, and financial forecasting.
Problems Solved: - This method helps in automating the process of designing neural network architectures, saving time and resources for researchers and developers.
Benefits: - By finding the optimal architecture for a neural network, this technology can improve the performance and efficiency of machine learning models.
Potential Commercial Applications: - "Optimizing Neural Network Architectures for Enhanced Performance in Machine Learning Applications"
Possible Prior Art: - One possible prior art could be the use of genetic algorithms to optimize neural network architectures.
Unanswered Questions: 1. How does the method handle complex tasks that require intricate neural network architectures? 2. Are there any limitations to the scalability of this approach when dealing with large datasets or models?
Original Abstract Submitted
A computer-implemented method for ascertaining an optimal architecture for a neural network that solves a given task in accordance with given boundary conditions and/or optimization goals. The method includes: providing a graph of the possible architectures of nodes and edges, wherein nodes correspond to data, edges correspond to parameterized operations to be carried out on the data, and a path which traverses the entire graph corresponds to an architecture; in a search phase, generating candidate architectures based on already known architectures, wherein the candidate architectures are similar but not identical to the known architectures in accordance with a predetermined criterion; evaluating the candidate architectures using the given boundary conditions and/or optimization goals;
