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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Coma704

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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Flawless

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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