Add to ORKGDeep Neural Networks (DNNs) have proven excellent performance and are very successful in image classification and object detection. Safety critical industries such as the automotive and aerospace industry aim to develop autonomous vehicles with the help of DNNs. In order to certify the usage of DNNs in safety critical systems, it is essential to prove the correctness of data within the system. In this thesis, the research is focused on investigating the sources of uncertainty, what effects various sources of uncertainty has on NNs, and how it is possible to reduce uncertainty within an NN. Probabilistic methods are used to implement an NN with uncertainty estimation to analyze and evaluate how the integrity of the NN is affected. By analyzi...
There are two large groups of sources of uncertainty at various stages of construction of neural net...
The proliferation of Deep Neural Networks has resulted in machine learning systems becoming increasi...
Uncertainty and confidence have been shown to be useful metrics in a wide variety of techniques prop...
Deep Neural Networks (DNN) are increasingly used as components of larger software systems that need ...
Modern software systems rely on Deep Neural Networks (DNN) when processing complex, unstructured inp...
Over the last decade, neural networks have reached almost every field of science and become a crucia...
This open access book brings together the latest developments from industry and research on automate...
Deep neural networks generally perform very well on giving accurate predictions, but they often lack...
Suppose data-driven black-box models, e.g., neural networks, should be used as components in safety-...
The breakout success of deep neural networks (NNs) in the 2010's marked a new era in the quest to bu...
This open access book brings together the latest developments from industry and research on automate...
A novel technique for the evaluation of neural network robustness against uncertainty using a nonpro...
CEA covered the scenario of UAV navigation through a set of gates with unknown locations using a DNN...
Deep learning, and in particular neural networks (NNs), have seen a surge in popularity over the pas...
Uncertainty quantification plays a critical role in the process of decision making and optimization ...
There are two large groups of sources of uncertainty at various stages of construction of neural net...
The proliferation of Deep Neural Networks has resulted in machine learning systems becoming increasi...
Uncertainty and confidence have been shown to be useful metrics in a wide variety of techniques prop...
Deep Neural Networks (DNN) are increasingly used as components of larger software systems that need ...
Modern software systems rely on Deep Neural Networks (DNN) when processing complex, unstructured inp...
Over the last decade, neural networks have reached almost every field of science and become a crucia...
This open access book brings together the latest developments from industry and research on automate...
Deep neural networks generally perform very well on giving accurate predictions, but they often lack...
Suppose data-driven black-box models, e.g., neural networks, should be used as components in safety-...
The breakout success of deep neural networks (NNs) in the 2010's marked a new era in the quest to bu...
This open access book brings together the latest developments from industry and research on automate...
A novel technique for the evaluation of neural network robustness against uncertainty using a nonpro...
CEA covered the scenario of UAV navigation through a set of gates with unknown locations using a DNN...
Deep learning, and in particular neural networks (NNs), have seen a surge in popularity over the pas...
Uncertainty quantification plays a critical role in the process of decision making and optimization ...
There are two large groups of sources of uncertainty at various stages of construction of neural net...
The proliferation of Deep Neural Networks has resulted in machine learning systems becoming increasi...
Uncertainty and confidence have been shown to be useful metrics in a wide variety of techniques prop...