18465627. DEVICE AND METHOD FOR TRAINING A VARIATIONAL AUTOENCODER simplified abstract (Robert Bosch GmbH)
Contents
- 1 DEVICE AND METHOD FOR TRAINING A VARIATIONAL AUTOENCODER
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DEVICE AND METHOD FOR TRAINING A VARIATIONAL AUTOENCODER - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
DEVICE AND METHOD FOR TRAINING A VARIATIONAL AUTOENCODER
Organization Name
Inventor(s)
Faris Janjos of Stuttgart (DE)
Lars Rosenbaum of Lahntal (DE)
Maxim Dolgov of Renningen (DE)
DEVICE AND METHOD FOR TRAINING A VARIATIONAL AUTOENCODER - A simplified explanation of the abstract
This abstract first appeared for US patent application 18465627 titled 'DEVICE AND METHOD FOR TRAINING A VARIATIONAL AUTOENCODER
Simplified Explanation
The abstract describes a computer-implemented method for training a machine learning system using an encoder and a decoder to determine intermediate representations and output signals based on a training input signal.
- The encoder determines a mean and variance/covariance of a latent distribution in a latent space.
- Sigma points are determined based on the intermediate representations.
- The decoder uses randomly sampled sigma points to determine the output signal.
- The machine learning system is adapted based on the loss value, which measures the difference between the training input signal and the output signal.
Potential Applications
This technology can be applied in various fields such as image recognition, natural language processing, and autonomous driving.
Problems Solved
This technology helps in improving the accuracy and efficiency of machine learning systems by optimizing the training process and enhancing the quality of output signals.
Benefits
The benefits of this technology include faster training times, better performance of machine learning models, and increased adaptability to different types of data.
Potential Commercial Applications
Potential commercial applications of this technology include developing advanced AI systems for healthcare diagnostics, financial forecasting, and personalized recommendations.
Possible Prior Art
One possible prior art for this technology could be the use of variational autoencoders in machine learning systems to generate latent representations and improve training processes.
What are the specific technical details of the encoder and decoder used in this method?
The specific technical details of the encoder include determining the mean and variance/covariance of the latent distribution in the latent space. The decoder uses randomly sampled sigma points to generate the output signal based on the intermediate representations.
How does this method compare to traditional machine learning training techniques in terms of efficiency and accuracy?
This method improves efficiency and accuracy by optimizing the training process through the use of sigma points and intermediate representations, leading to better performance of machine learning models.
Original Abstract Submitted
A computer-implemented method for training a machine learning system. The training includes: determining, by an encoder of the machine learning system and based on a training input signal, a first intermediate representation characterizing a mean of a latent distribution of a latent space and a second intermediate representation characterizing a variance and/or covariance of the latent distribution; determining, based on the first intermediate representation and the second intermediate representation, a plurality of sigma points with respect to the latent distribution; determining an output signal, wherein the output signal is determined by providing a randomly sampled sigma point of the plurality of sigma points to a decoder of the machine learning system; adapting the machine learning system based on a loss value, wherein the loss value characterizes a difference between the training input signal and the output signal.
