Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

Table of Contents

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   Book Overview
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   Chapter 1 Towards Explainable Artificial Intelligence
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   Chapter 2 Transparency: Motivations and Challenges
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   Chapter 3 Interpretability in Intelligent Systems – A New Concept?
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   Chapter 4 Understanding Neural Networks via Feature Visualization: A Survey
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   Chapter 5 Interpretable Text-to-Image Synthesis with Hierarchical Semantic Layout Generation
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   Chapter 6 Unsupervised Discrete Representation Learning
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   Chapter 7 Towards Reverse-Engineering Black-Box Neural Networks
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   Chapter 8 Explanations for Attributing Deep Neural Network Predictions
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    Chapter 9 Gradient-Based Attribution Methods
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    Chapter 10 Layer-Wise Relevance Propagation: An Overview
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    Chapter 11 Explaining and Interpreting LSTMs
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    Chapter 12 Comparing the Interpretability of Deep Networks via Network Dissection
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    Chapter 13 Gradient-Based Vs. Propagation-Based Explanations: An Axiomatic Comparison
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    Chapter 14 The (Un)reliability of Saliency Methods
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    Chapter 15 Visual Scene Understanding for Autonomous Driving Using Semantic Segmentation
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    Chapter 16 Understanding Patch-Based Learning of Video Data by Explaining Predictions
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    Chapter 17 Quantum-Chemical Insights from Interpretable Atomistic Neural Networks
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    Chapter 18 Interpretable Deep Learning in Drug Discovery
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    Chapter 19 NeuralHydrology – Interpreting LSTMs in Hydrology
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    Chapter 20 Feature Fallacy: Complications with Interpreting Linear Decoding Weights in fMRI
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    Chapter 21 Current Advances in Neural Decoding
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    Chapter 22 Software and Application Patterns for Explanation Methods