View source for 18443711. RAPID ESTIMATION OF THE UNCERTAINTY OF THE OUTPUT OF A NEURAL TASK NETWORK simplified abstract (Robert Bosch GmbH)

=RAPID ESTIMATION OF THE UNCERTAINTY OF THE OUTPUT OF A NEURAL TASK NETWORK=


==Organization Name==

[[:Category:Robert Bosch GmbH|Robert Bosch GmbH]]


[[Category:Robert Bosch GmbH]]

==Inventor(s)==

[[:Category:Lydia Gauerhof of Sindelfingen (DE)|Lydia Gauerhof of Sindelfingen (DE)]][[Category:Lydia Gauerhof of Sindelfingen (DE)]]

[[:Category:Bangyu Zhu of Leonberg (DE)|Bangyu Zhu of Leonberg (DE)]][[Category:Bangyu Zhu of Leonberg (DE)]]

[[:Category:Christoph Schorn of Benningen Am Neckar (DE)|Christoph Schorn of Benningen Am Neckar (DE)]][[Category:Christoph Schorn of Benningen Am Neckar (DE)]]

==RAPID ESTIMATION OF THE UNCERTAINTY OF THE OUTPUT OF A NEURAL TASK NETWORK - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18443711 titled 'RAPID ESTIMATION OF THE UNCERTAINTY OF THE OUTPUT OF A NEURAL TASK NETWORK

**Simplified Explanation:**
This patent application describes a method for training a measurement network to determine the uncertainty of an already trained task network. Training data is fed to multiple versions of the task network or multiple times to a probabilistic task network to generate various outputs. The measurement network then predicts the distribution of these outputs and evaluates the consistency with a predefined cost function.

**Key Features and Innovation:**
* Training a measurement network to assess uncertainty in a trained task network.
* Utilizing multiple versions of a deterministic task network or multiple runs of a probabilistic task network to generate diverse outputs.
* Predicting the distribution of outputs using the measurement network.
* Evaluating the consistency of the prediction with a predefined cost function.
* Optimizing network parameters to enhance the measurement network's behavior.

**Potential Applications:**
This technology can be applied in various fields such as finance, healthcare, weather forecasting, and quality control to assess the uncertainty of predictions made by task networks.

**Problems Solved:**
This technology addresses the need for accurate uncertainty estimation in trained task networks, which is crucial for making informed decisions based on the predictions generated.

**Benefits:**
* Improved accuracy in uncertainty estimation.
* Enhanced decision-making based on more reliable predictions.
* Increased trust in the outputs of task networks.

**Commercial Applications:**
Potential commercial applications include financial risk assessment, medical diagnosis support systems, predictive maintenance in manufacturing, and optimizing supply chain management.

**Questions about the Technology:**
1. How does this method improve the reliability of predictions made by task networks?
2. What are the key advantages of using a measurement network to assess uncertainty in trained task networks?


==Original Abstract Submitted==

A method for training a measurement network which ascertains the uncertainty of an already trained task network. In the method: each training record of measurement data from a training data set is fed to a plurality of modifications of a deterministic task network, or fed multiple times to a probabilistic task network, and thus mapped onto a plurality of outputs; each training record is fed to the measurement network and mapped onto a prediction of the distribution of the plurality of outputs, wherein the processing chain of the measurement network includes a part of the processing chain of the task network; a predefined cost function evaluates the extent to which the prediction of the distribution is consistent with the outputs; and network parameters which characterize the behavior of that part of the measurement network that does not belong to the processing chain of the task network are optimized.



[[Category:G06N3/08]]
[[Category:CPC_G06N3/08]]