Explainable Deep Learning AI: Methods and Challenges
Editat de Jenny Benois-Pineau, Romain Bourqui, Dragutin Petkovic, Georges Quenoten Limba Engleză Paperback – 23 feb 2023
The groups of methods such as back-propagation and perturbation-based methods are explained, and the application to various kinds of data classification are presented.
- Provides an overview of main approaches to Explainable Artificial Intelligence (XAI) in the Deep Learning realm, including the most popular techniques and their use, concluding with challenges and exciting future directions of XAI
- Explores the latest developments in general XAI methods for Deep Learning
- Explains how XAI for Deep Learning is applied to various domains like images, medicine and natural language processing
- Provides an overview of how XAI systems are tested and evaluated, specially with real users, a critical need in XAI
Preț: 650.55 lei
Preț vechi: 813.18 lei
-20% Nou
Puncte Express: 976
Preț estimativ în valută:
124.50€ • 129.33$ • 103.42£
124.50€ • 129.33$ • 103.42£
Carte disponibilă
Livrare economică 13-27 ianuarie 25
Livrare express 27 decembrie 24 - 02 ianuarie 25 pentru 36.36 lei
Preluare comenzi: 021 569.72.76
Specificații
ISBN-13: 9780323960984
ISBN-10: 0323960987
Pagini: 346
Dimensiuni: 191 x 235 x 23 mm
Greutate: 0.59 kg
Editura: ELSEVIER SCIENCE
ISBN-10: 0323960987
Pagini: 346
Dimensiuni: 191 x 235 x 23 mm
Greutate: 0.59 kg
Editura: ELSEVIER SCIENCE
Public țintă
This book is intended for researchers, PhD students, and practitioners in the area of Explainable Artificial Intelligence (XAI) specifically related to Deep Learning AI MethodsCuprins
1. Introduction
2. Explainable Deep Learning: Methods, Concepts and New Developments
3. Compact Visualization of DNN Classification Performances for Interpretation and Improvement
4. Explaining How Deep Neural Networks Forget by Deep Visualization
5. Characterizing a scene recognition model by identifying the effect of input features via semantic- wise attribution
6. A Feature Understanding Method for Explanation of Image Classification by Convolutional Neural Networks
7. Explainable Deep Learning for decrypting disease signature in Multiple Sclerosis
8. Explanation of CNN Image Classifiers with Hiding Parts
9. Remove to Improve?
10. Explaining CNN classifier using Association Rule Mining Methods on time-series
11. A Methodology to compare XAI Explanations on Natural Language Processing
12. Improving Malware Detection with Explainable Machine Learning
13. AI Explainability. A Bridge between Machine Vision and Natural Language Processing
14. Explainable Deep Learning for Multimedia Indexing and Retrieval
15. User Tests and Techniques for the Post-Hoc Explainability of Deep Learning Models
16. Conclusion
2. Explainable Deep Learning: Methods, Concepts and New Developments
3. Compact Visualization of DNN Classification Performances for Interpretation and Improvement
4. Explaining How Deep Neural Networks Forget by Deep Visualization
5. Characterizing a scene recognition model by identifying the effect of input features via semantic- wise attribution
6. A Feature Understanding Method for Explanation of Image Classification by Convolutional Neural Networks
7. Explainable Deep Learning for decrypting disease signature in Multiple Sclerosis
8. Explanation of CNN Image Classifiers with Hiding Parts
9. Remove to Improve?
10. Explaining CNN classifier using Association Rule Mining Methods on time-series
11. A Methodology to compare XAI Explanations on Natural Language Processing
12. Improving Malware Detection with Explainable Machine Learning
13. AI Explainability. A Bridge between Machine Vision and Natural Language Processing
14. Explainable Deep Learning for Multimedia Indexing and Retrieval
15. User Tests and Techniques for the Post-Hoc Explainability of Deep Learning Models
16. Conclusion