Towards Explainability of non-Convolutional Neural Networks

Jonas Doese; Torben Weis

doi:10.1145/3460418.3479313

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JOURNAL ARTICLE

Towards Explainability of non-Convolutional Neural Networks

Jonas Doese Torben Weis

Year: 2021 Pages: 100-103

DOI: 10.1145/3460418.3479313

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Abstract

Artificial intelligence has risen in popularity in research and applications in the past years. Explainability is a topic that has been proven to work on human interpretable data like images or sentences, but the research is narrow whenever such data is missing.

Keywords:

Popularity Convolutional neural network Computer science Artificial intelligence Work (physics) Machine learning Data science Artificial neural network Engineering Psychology

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Explainable Artificial Intelligence (XAI)

Physical Sciences → Computer Science → Artificial Intelligence

Adversarial Robustness in Machine Learning

Physical Sciences → Computer Science → Artificial Intelligence

Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Towards Explainability of non-Convolutional Neural Networks

Abstract

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Citation History

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