Synopsis
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.
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