Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
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...
Συγγραφή απο Οργανισμό/Αρχή: | |
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Άλλοι συγγραφείς: | , , , , |
Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
Γλώσσα: | English |
Έκδοση: |
Cham :
Springer International Publishing : Imprint: Springer,
2019.
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Έκδοση: | 1st ed. 2019. |
Σειρά: | Lecture Notes in Artificial Intelligence ;
11700 |
Θέματα: | |
Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- 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.