This paper provides an entry point to the problem of interpreting a deep neural network
model and explaining its predictions. It is based on a tutorial given at ICASSP 2017. As a …

For a machine learning model to generalize well, one needs to ensure that its decisions are
supported by meaningful patterns in the input data. A prerequisite is however for the model …

We present the application of layer-wise relevance propagation to several deep neural
networks such as the BVLC reference neural net and googlenet trained on ImageNet and …

Deep neural networks (DNNs) have demonstrated impressive performance in complex
machine learning tasks such as image classification or speech recognition. However, due to …

While neural networks have acted as a strong unifying force in the design of modern AI
systems, the neural network architectures themselves remain highly heterogeneous due to …

Interpretability is a fundamental property for the acceptance of machine learning models in
highly regulated areas. Recently, deep neural networks gained the attention of the scientific …

The problem of explaining deep learning models, and model predictions generally, has
attracted intensive interest recently. Many successful approaches forgo global …

Deep neural networks have been well-known for their superb handling of various machine
learning and artificial intelligence tasks. However, due to their over-parameterized black-box …

With the availability of large databases and recent improvements in deep learning
methodology, the performance of AI systems is reaching or even exceeding the human level …

A substantial amount of research has been carried out in Explainable Artificial Intelligence
(XAI) models, especially in those which explain the deep architectures of neural networks. A …