Deepmind technologies limited (20240176982). TRAINING GRAPH NEURAL NETWORKS USING A DE-NOISING OBJECTIVE simplified abstract
Contents
- 1 TRAINING GRAPH NEURAL NETWORKS USING A DE-NOISING OBJECTIVE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 TRAINING GRAPH NEURAL NETWORKS USING A DE-NOISING OBJECTIVE - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Graph Neural Network Training
- 1.13 Original Abstract Submitted
TRAINING GRAPH NEURAL NETWORKS USING A DE-NOISING OBJECTIVE
Organization Name
Inventor(s)
Jonathan William Godwin of London (GB)
Peter William Battaglia of London (GB)
Kevin Michael Schaarschmidt of Cambridge (GB)
TRAINING GRAPH NEURAL NETWORKS USING A DE-NOISING OBJECTIVE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240176982 titled 'TRAINING GRAPH NEURAL NETWORKS USING A DE-NOISING OBJECTIVE
Simplified Explanation
The patent application describes methods, systems, and apparatus for training a neural network with graph neural network layers by generating data defining a graph, processing the data using the neural network layers, and generating de-noising predictions for nodes with modified feature representations.
Key Features and Innovation
- Training a neural network with graph neural network layers
- Generating data defining a graph
- Processing data to generate updated node embeddings
- Generating de-noising predictions for nodes with modified feature representations
Potential Applications
The technology can be applied in various fields such as image recognition, natural language processing, and recommendation systems.
Problems Solved
The technology addresses the challenge of training neural networks with graph structures and noisy data.
Benefits
- Improved accuracy in data processing
- Enhanced performance in graph-based tasks
- Robustness to noise in feature representations
Commercial Applications
Graph-based Machine Learning Systems
This technology can be utilized in developing advanced machine learning systems for various industries, including healthcare, finance, and e-commerce.
Prior Art
There is prior art related to graph neural networks and noise reduction techniques in neural networks.
Frequently Updated Research
Research on graph neural networks and noise reduction techniques in neural networks is continuously evolving.
Questions about Graph Neural Network Training
How does this technology improve the efficiency of graph-based tasks?
This technology enhances the efficiency by generating de-noising predictions for nodes with modified feature representations, leading to more accurate results.
What are the potential limitations of using graph neural network layers in training neural networks?
One potential limitation could be the complexity of implementing and optimizing graph neural network layers for specific tasks.
Original Abstract Submitted
methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a neural network that includes one or more graph neural network layers. in one aspect, a method comprises: generating data defining a graph, comprising: generating a respective final feature representation for each node, wherein, for each of one or more of the nodes, the respective final feature representation is a modified feature representation that is generated from a respective feature representation for the node using respective noise; processing the data defining the graph using one or more of the graph neural network layers of the neural network to generate a respective updated node embedding of each node; and processing, for each of one or more of the nodes having modified feature representations, the updated node embedding of the node to generate a respective de-noising prediction for the node.
