Deepmind technologies limited (20240202511). GATED LINEAR NETWORKS simplified abstract
Contents
GATED LINEAR NETWORKS
Organization Name
Inventor(s)
Agnieszka Grabska-barwinska of London (GB)
Christopher Mattern of London (GB)
Avishkar Bhoopchand of London (GB)
Joel William Veness of London (GB)
GATED LINEAR NETWORKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240202511 titled 'GATED LINEAR NETWORKS
The patent application describes methods, systems, and apparatus for a neural network system with one or more gated linear networks.
- Each gated linear network corresponds to a data value in an output data sample and generates a network probability output defining a probability distribution over possible values for the data value.
- Gated linear networks consist of multiple layers, including gated linear layers with nodes that receive inputs and side information, combine inputs based on weights defined by the side information, and generate node probability outputs.
Potential Applications:
- Natural language processing
- Image recognition
- Speech recognition
- Anomaly detection
- Recommendation systems
Problems Solved:
- Improved accuracy in predicting data values
- Enhanced performance in complex data modeling
- Efficient processing of large datasets
- Better handling of uncertainty in data
Benefits:
- Higher precision in data analysis
- Faster decision-making based on accurate predictions
- Enhanced user experience in various applications
- Increased efficiency in machine learning tasks
Commercial Applications:
- AI-powered chatbots for customer service
- Personalized recommendation engines for e-commerce
- Fraud detection systems for financial institutions
- Autonomous vehicles for improved safety and navigation
Questions about Gated Linear Networks: 1. How do gated linear networks improve the accuracy of data predictions?
Gated linear networks use probability distributions to provide a more nuanced understanding of possible data values, leading to more accurate predictions.
2. What are the key advantages of using gated linear networks in neural network systems?
Gated linear networks offer improved performance in handling complex data structures and uncertainty, making them valuable for various machine learning tasks.
Original Abstract Submitted
methods, systems, and apparatus, including computer programs encoded on computer storage media, for a neural network system comprising one or more gated linear networks. a system includes: one or more gated linear networks, wherein each gated linear network corresponds to a respective data value in an output data sample and is configured to generate a network probability output that defines a probability distribution over possible values for the corresponding data value, wherein each gated linear network comprises a plurality of layers, wherein the plurality of layers comprises a plurality of gated linear layers, wherein each gated linear layer has one or more nodes, and wherein each node is configured to: receive a plurality of inputs, receive side information for the node; combine the plurality of inputs according to a set of weights defined by the side information, and generate and output a node probability output for the corresponding data value.
