Add to ORKGThe evolution regarding Intelligent Systems Development, especially the advances in the field of Deep Learning Networks facilitate the design of complex prediction systems in multiple fields like image recognition or time series prediction spanning over different sectors like manufacturing or service industry. The accuracy achieved in those complex systems comes at the price of total in non-transparency of model results. In order to tackle that research gap we propose a systematic scheme that proposes several methods for interpreting and visualizing deep neural network results. We then characterize the methods and give a comprehensive overview over the current state of the art and provide limitations and further research proposals
The application of Deep Neural Networks (DNNs) to a broad variety of tasks demands methods for copin...
The application of Deep Neural Networks (DNNs) to a broad variety of tasks demands methods for copin...
Although deep neural networks have achieved state-of-the-art performance in several artificial intel...
Deep neural networks are notoriously black boxes that defy human interpretations. The lack of unders...
Deep Learning is a state-of-the-art technique to make inference on extensive or complex data. As a b...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Comp...
This article presents the prediction difference analysis method for visualizing the response of a de...
Artificial neural networks are computer software or hardware models inspired by the structure and be...
The application of Deep Neural Networks (DNNs) to a broad variety of tasks demands methods for copin...
Practical deployment of deep neural networks has become widespread in the last decade due to their a...
As machine learning algorithms are increasingly applied to high impact yet high risk tasks, such as ...
As machine learning algorithms are increasingly applied to high impact yet high risk tasks, such as ...
Neural networks, in the context of deep learning, show much promise in becoming an important tool wi...
The application of Deep Neural Networks (DNNs) to a broad variety of tasks demands methods for copin...
The application of Deep Neural Networks (DNNs) to a broad variety of tasks demands methods for copin...
The application of Deep Neural Networks (DNNs) to a broad variety of tasks demands methods for copin...
The application of Deep Neural Networks (DNNs) to a broad variety of tasks demands methods for copin...
Although deep neural networks have achieved state-of-the-art performance in several artificial intel...
Deep neural networks are notoriously black boxes that defy human interpretations. The lack of unders...
Deep Learning is a state-of-the-art technique to make inference on extensive or complex data. As a b...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Comp...
This article presents the prediction difference analysis method for visualizing the response of a de...
Artificial neural networks are computer software or hardware models inspired by the structure and be...
The application of Deep Neural Networks (DNNs) to a broad variety of tasks demands methods for copin...
Practical deployment of deep neural networks has become widespread in the last decade due to their a...
As machine learning algorithms are increasingly applied to high impact yet high risk tasks, such as ...
As machine learning algorithms are increasingly applied to high impact yet high risk tasks, such as ...
Neural networks, in the context of deep learning, show much promise in becoming an important tool wi...
The application of Deep Neural Networks (DNNs) to a broad variety of tasks demands methods for copin...
The application of Deep Neural Networks (DNNs) to a broad variety of tasks demands methods for copin...
The application of Deep Neural Networks (DNNs) to a broad variety of tasks demands methods for copin...
The application of Deep Neural Networks (DNNs) to a broad variety of tasks demands methods for copin...
Although deep neural networks have achieved state-of-the-art performance in several artificial intel...