DeepMind Technologies Limited (20240249146). Using Hierarchical Representations for Neural Network Architecture Searching simplified abstract
Contents
Using Hierarchical Representations for Neural Network Architecture Searching
Organization Name
Inventor(s)
Chrisantha Thomas Fernando of London (GB)
Koray Kavukcuoglu of London (GB)
Hanxiao Liu of Santa Clara CA (US)
Using Hierarchical Representations for Neural Network Architecture Searching - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240249146 titled 'Using Hierarchical Representations for Neural Network Architecture Searching
The abstract describes a computer-implemented method for automatically determining a neural network architecture by representing it as a hierarchical set of directed acyclic graphs in multiple levels, with nodes connected by directed edges indicating operations performed on outputs.
- The method involves generating new sample neural network architectures and evaluating their fitness.
- Modifications are made by selecting a level, two nodes at that level, and adding, removing, or modifying an edge between them based on operations associated with lower levels.
- Each level is associated with a set of operations, with primitive operations selected at the lowest level.
Potential Applications: - Automated neural network architecture design - Optimization of neural network performance - Streamlining the process of developing neural networks
Problems Solved: - Manual determination of neural network architectures - Time-consuming trial and error in architecture design - Lack of optimization in neural network performance
Benefits: - Increased efficiency in neural network development - Improved performance through optimized architectures - Reduction in human error in architecture design
Commercial Applications: Title: "Automated Neural Network Architecture Design: Revolutionizing AI Development" This technology could be utilized in industries such as: - AI development companies - Research institutions - Tech companies focusing on machine learning applications
Prior Art: Readers can explore prior research on automated neural network architecture design, directed acyclic graphs in machine learning, and optimization techniques for neural networks.
Frequently Updated Research: Stay updated on advancements in automated neural network architecture design, optimization algorithms for neural networks, and the application of directed acyclic graphs in machine learning.
Questions about Automated Neural Network Architecture Design: 1. How does this method compare to traditional manual neural network architecture design processes? 2. What are the potential limitations of using automated methods for determining neural network architectures?
Original Abstract Submitted
a computer-implemented method for automatically determining a neural network architecture represents a neural network architecture as a data structure defining a hierarchical set of directed acyclic graphs in multiple levels. each graph has an input, an output, and a plurality of nodes between the input and the output. at each level, a corresponding set of the nodes are connected pairwise by directed edges which indicate operations performed on outputs of one node to generate an input to another node. each level is associated with a corresponding set of operations. at a lowest level, the operations associated with each edge are selected from a set of primitive operations. the method includes repeatedly generating new sample neural network architectures, and evaluating their fitness. the modification is performed by selecting a level, selecting two nodes at that level, and modifying, removing or adding an edge between those nodes according to operations associated with lower levels of the hierarchy.
