Titlebook: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning; Wojciech Samek,Grégoire Montavon,Klaus-Robert Müll Book 2019 Sprin

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Explaining and Interpreting LSTMshly heterogeneous due to the variety of tasks to be solved. In this chapter, we explore how to adapt the Layer-wise Relevance Propagation (LRP) technique used for explaining the predictions of feed-forward networks to the LSTM architecture used for sequential data modeling and forecasting. The speci

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Gradient-Based Vs. Propagation-Based Explanations: An Axiomatic Comparisonses the question whether the produced explanations are reliable. In this chapter, we consider two popular explanation techniques, one based on gradient computation and one based on a propagation mechanism. We evaluate them using three “axiomatic” properties: ., ., and .. These properties are tested

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https://doi.org/10.1007/978-3-030-03213-5put image) are responsible for a model’s output (i.e., a CNN classifier’s object class prediction). We first introduced these contributions in?[.]. We also briefly survey existing visual attribution methods and highlight how they faith to be both . and ..

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Kathy L. Bradley-Klug,Emily Shaffer-Hudkinsn the scenario of the train-from-scratch and in the stage of the fine-tuning between data sources. Our results highlight that interpretability is an important property of deep neural networks that provides new insights into their hierarchical structure.

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Book 2019ing factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For?sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper

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