Deepmind technologies limited (20240273333). LEARNING OBSERVATION REPRESENTATIONS BY PREDICTING THE FUTURE IN LATENT SPACE simplified abstract
Contents
- 1 LEARNING OBSERVATION REPRESENTATIONS BY PREDICTING THE FUTURE IN LATENT SPACE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 LEARNING OBSERVATION REPRESENTATIONS BY PREDICTING THE FUTURE IN LATENT SPACE - 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 Encoder Neural Network Training
- 1.13 Original Abstract Submitted
LEARNING OBSERVATION REPRESENTATIONS BY PREDICTING THE FUTURE IN LATENT SPACE
Organization Name
Inventor(s)
Aaron Gerard Antonius Van Den Oord of London (GB)
LEARNING OBSERVATION REPRESENTATIONS BY PREDICTING THE FUTURE IN LATENT SPACE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240273333 titled 'LEARNING OBSERVATION REPRESENTATIONS BY PREDICTING THE FUTURE IN LATENT SPACE
Simplified Explanation
The patent application describes methods, systems, and apparatus for training an encoder neural network to generate a latent representation of input observations.
- Obtaining a sequence of observations
- Processing each observation using the encoder neural network to generate a latent representation
- Generating a context latent representation for given observations
- Estimating latent representations of particular observations following the given observation
Key Features and Innovation
- Training an encoder neural network to process input observations
- Generating latent representations of observations in a sequence
- Estimating latent representations of future observations based on context latent representations
Potential Applications
This technology can be applied in various fields such as image recognition, natural language processing, and data compression.
Problems Solved
This technology addresses the need for efficient processing and representation of sequential observations.
Benefits
- Improved accuracy in generating latent representations
- Enhanced efficiency in processing sequences of observations
- Potential for better performance in various machine learning tasks
Commercial Applications
- Image recognition systems
- Speech recognition software
- Data compression algorithms
Prior Art
Readers can explore prior research on encoder neural networks, latent representations, and sequential data processing.
Frequently Updated Research
Stay updated on advancements in encoder neural network training techniques and applications.
Questions about Encoder Neural Network Training
How does training an encoder neural network differ from training other types of neural networks?
Training an encoder neural network involves learning to generate latent representations of input observations, which is specific to this type of network architecture.
What are some potential challenges in training an encoder neural network effectively?
Challenges may include selecting appropriate hyperparameters, dealing with overfitting, and ensuring the network captures relevant information in the latent representations.
Original Abstract Submitted
methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an encoder neural network that is configured to process an input observation to generate a latent representation of the input observation. in one aspect, a method includes: obtaining a sequence of observations; for each observation in the sequence of observations, processing the observation using the encoder neural network to generate a latent representation of the observation; for each of one or more given observations in the sequence of observations: generating a context latent representation of the given observation; and generating, from the context latent representation of the given observation, a respective estimate of the latent representations of one or more particular observations that are after the given observation in the sequence of observations.
