View source for Robert bosch gmbh (20240281655). RAPID ESTIMATION OF THE UNCERTAINTY OF THE OUTPUT OF A NEURAL TASK NETWORK simplified abstract

=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 20240281655 titled 'RAPID ESTIMATION OF THE UNCERTAINTY OF THE OUTPUT OF A NEURAL TASK NETWORK

**Simplified Explanation:**
The 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, and the measurement network predicts the distribution of outputs. A cost function evaluates the prediction consistency, and network parameters are optimized.

**Key Features and Innovation:**
- Training a measurement network to ascertain uncertainty of a trained task network.
- Utilizing multiple versions of a deterministic task network or a probabilistic task network.
- Predicting the distribution of outputs based on training data.
- Evaluating prediction consistency with a cost function.
- Optimizing network parameters for improved performance.

**Potential Applications:**
- Quality control in manufacturing processes.
- Risk assessment in financial forecasting.
- Medical diagnosis and prognosis.
- Environmental monitoring and prediction.
- Autonomous vehicle navigation and decision-making.

**Problems Solved:**
- Enhancing the accuracy of uncertainty estimation.
- Improving reliability of predictions in complex systems.
- Streamlining the training process for measurement networks.
- Addressing the need for robust uncertainty quantification methods.
- Enhancing the performance of task networks in various applications.

**Benefits:**
- Increased confidence in decision-making based on network predictions.
- Improved risk management strategies.
- Enhanced system performance and reliability.
- Cost savings through optimized network parameters.
- Potential for innovation and advancement in various industries.

**Commercial Applications:**
Title: "Enhanced Uncertainty Estimation Method for Improved Decision-Making"
This technology can be applied in industries such as finance, healthcare, manufacturing, transportation, and environmental monitoring. Companies can use this method to optimize processes, reduce risks, and improve overall performance in their operations.

**Prior Art:**
Prior research in machine learning, neural networks, and uncertainty quantification methods can provide valuable insights into similar approaches to training measurement networks for uncertainty estimation in task networks.

**Frequently Updated Research:**
Researchers are continuously exploring new techniques and algorithms to enhance uncertainty estimation in neural networks and improve the reliability of predictions in various applications. Stay updated on advancements in machine learning and artificial intelligence for the latest developments in this field.

**Questions about the Technology:**
1. How does this method compare to traditional uncertainty estimation techniques in neural networks?
2. What are the potential limitations or challenges in implementing this training approach for measurement 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]]