Deepmind technologies limited (20240354566). GENERATING DISCRETE LATENT REPRESENTATIONS OF INPUT DATA ITEMS simplified abstract
Contents
GENERATING DISCRETE LATENT REPRESENTATIONS OF INPUT DATA ITEMS
Organization Name
Inventor(s)
Koray Kavukcuoglu of London (GB)
Aaron Gerard Antonius Van Den Oord of London (GB)
GENERATING DISCRETE LATENT REPRESENTATIONS OF INPUT DATA ITEMS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240354566 titled 'GENERATING DISCRETE LATENT REPRESENTATIONS OF INPUT DATA ITEMS
Simplified Explanation: This patent application describes methods, systems, and apparatus for generating discrete latent representations of input data items using an encoder neural network.
- The method involves receiving an input data item and providing it as input to an encoder neural network to obtain an encoder output.
- A discrete latent representation of the input data item is generated by determining a latent embedding vector nearest to the encoded vector for each latent variable.
Key Features and Innovation:
- Utilizes an encoder neural network to generate discrete latent representations of input data items.
- Determines latent embedding vectors from a set in memory to create the latent representation.
- Focuses on finding the nearest latent embedding vector to the encoded vector for each latent variable.
Potential Applications:
- Image recognition and classification.
- Natural language processing for text analysis.
- Anomaly detection in data sets.
Problems Solved:
- Efficiently generating discrete latent representations of input data items.
- Improving the accuracy and effectiveness of latent variable modeling.
Benefits:
- Enhanced data representation for machine learning models.
- Improved performance in various data analysis tasks.
- Potential for more accurate predictions and classifications.
Commercial Applications:
- Enhanced data processing and analysis tools for businesses.
- Improved machine learning algorithms for various industries.
Questions about the Technology: 1. How does this technology improve upon existing methods of generating latent representations? 2. What are the potential limitations or challenges of implementing this technology in real-world applications?
Frequently Updated Research: There may be ongoing research in the field of neural networks and latent variable modeling that could impact the development and application of this technology.
Original Abstract Submitted
methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating discrete latent representations of input data items. one of the methods includes receiving an input data item; providing the input data item as input to an encoder neural network to obtain an encoder output for the input data item; and generating a discrete latent representation of the input data item from the encoder output, comprising: for each of the latent variables, determining, from a set of latent embedding vectors in the memory, a latent embedding vector that is nearest to the encoded vector for the latent variable.
