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| Chapter title |
The (Un)reliability of Saliency Methods
|
|---|---|
| Chapter number | 14 |
| Book title |
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
|
| Published by |
Springer, Cham, September 2019
|
| DOI | 10.1007/978-3-030-28954-6_14 |
| Book ISBNs |
978-3-03-028953-9, 978-3-03-028954-6
|
| Authors |
Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof T. Schütt, Sven Dähne, Dumitru Erhan, Been Kim |
Mendeley readers
The data shown below were compiled from readership statistics for 451 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 451 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 119 | 26% |
| Student > Master | 96 | 21% |
| Researcher | 64 | 14% |
| Student > Bachelor | 43 | 10% |
| Student > Doctoral Student | 14 | 3% |
| Other | 33 | 7% |
| Unknown | 82 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 227 | 50% |
| Engineering | 56 | 12% |
| Mathematics | 9 | 2% |
| Physics and Astronomy | 8 | 2% |
| Agricultural and Biological Sciences | 6 | 1% |
| Other | 48 | 11% |
| Unknown | 97 | 22% |