20240028936. DEVICE AND COMPUTER-IMPLEMENTED METHOD FOR MACHINE LEARNING simplified abstract (Robert Bosch GmbH)
Contents
DEVICE AND COMPUTER-IMPLEMENTED METHOD FOR MACHINE LEARNING
Organization Name
Inventor(s)
Christoph Zimmer of Stuttgart (DE)
Matthias Bitzer of Stuttgart (DE)
DEVICE AND COMPUTER-IMPLEMENTED METHOD FOR MACHINE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240028936 titled 'DEVICE AND COMPUTER-IMPLEMENTED METHOD FOR MACHINE LEARNING
Simplified Explanation
The patent application describes a device and method for machine learning using a probabilistic model, such as a Gaussian process or a Bayesian neural network. The model is defined by at least one hyperparameter.
- The model is defined as a function of at least one hyperparameter, such as the Gaussian process or the Bayesian neural network.
- In each iteration, an instruction for a measurement is determined based on the model.
- A posteriori distribution over values for the hyperparameter is determined based on the first measurement.
- In another iteration, an instruction for a second measurement is determined based on the model.
- At least one value of the hyperparameter is determined based on the second measurement.
Potential applications of this technology:
- Predictive modeling: The probabilistic model can be used to make predictions based on measurements and update the model accordingly.
- Anomaly detection: The model can identify anomalies or outliers in the data based on the measurements.
- Optimization: The model can be used to optimize certain parameters or processes based on the measurements.
Problems solved by this technology:
- Uncertainty estimation: The probabilistic model allows for estimating the uncertainty associated with the predictions or measurements.
- Hyperparameter tuning: The method provides a way to determine the optimal values for the hyperparameters of the model.
- Data efficiency: The iterative approach allows for efficient use of data by updating the model based on new measurements.
Benefits of this technology:
- Improved accuracy: The probabilistic model can provide more accurate predictions by considering uncertainty and updating the model based on measurements.
- Flexibility: The method can be applied to different types of probabilistic models, allowing for flexibility in the choice of model.
- Efficient use of data: The iterative approach allows for efficient use of data by updating the model based on new measurements, reducing the need for large amounts of data.
Original Abstract Submitted
a device and computer-implemented method for machine learning. a probabilistic model is provided, in particular a model that includes a probability distribution, preferably a gaussian process or a bayesian neural network, the model being defined as a function of at least one hyperparameter, in particular of the gaussian process or of the bayesian neural network. in one iteration, an instruction for a first measurement is determined and output as a function of the model. for the at least one hyperparameter an a posteriori distribution over values for the at least one hyperparameter being determined as a function of the first measurement. in another iteration, an instruction for a second measurement is determined and output as a function of the model. at least one value of the at least one hyperparameter is determined as a function of the second measurement.
