Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

• Corporate Author: SpringerLink (Online service)
• Other Authors: Samek, Wojciech (Editor, http://id.loc.gov/vocabulary/relators/edt), Montavon, Grégoire (Editor, http://id.loc.gov/vocabulary/relators/edt), Vedaldi, Andrea (Editor, http://id.loc.gov/vocabulary/relators/edt), Hansen, Lars Kai (Editor, http://id.loc.gov/vocabulary/relators/edt), Müller, Klaus-Robert (Editor, http://id.loc.gov/vocabulary/relators/edt)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Edition: 1st ed. 2019.
• Series: Lecture Notes in Artificial Intelligence ; 11700
• Subjects: Artificial intelligence.; Optical data processing.; Computers.; Computer security.; Computer organization.; Artificial Intelligence.; Image Processing and Computer Vision.; Computing Milieux.; Systems and Data Security.; Computer Systems Organization and Communication Networks.

The development of "intelligent" systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to "intell...

Table of Contents:

• Towards Explainable Artificial Intelligence
• Transparency: Motivations and Challenges
• Interpretability in Intelligent Systems: A New Concept?
• Understanding Neural Networks via Feature Visualization: A Survey
• Interpretable Text-to-Image Synthesis with Hierarchical Semantic Layout Generation
• Unsupervised Discrete Representation Learning
• Towards Reverse-Engineering Black-Box Neural Networks
• Explanations for Attributing Deep Neural Network Predictions
• Gradient-Based Attribution Methods
• Layer-Wise Relevance Propagation: An Overview
• Explaining and Interpreting LSTMs
• Comparing the Interpretability of Deep Networks via Network Dissection
• Gradient-Based vs. Propagation-Based Explanations: An Axiomatic Comparison
• The (Un)reliability of Saliency Methods
• Visual Scene Understanding for Autonomous Driving Using Semantic Segmentation
• Understanding Patch-Based Learning of Video Data by Explaining Predictions
• Quantum-Chemical Insights from Interpretable Atomistic Neural Networks
• Interpretable Deep Learning in Drug Discovery
• Neural Hydrology: Interpreting LSTMs in Hydrology
• Feature Fallacy: Complications with Interpreting Linear Decoding Weights in fMRI
• Current Advances in Neural Decoding
• Software and Application Patterns for Explanation Methods.