Name
Abstract | ||
|---|---|---|
Neural networks are one of the most investigated and widely used techniques in Machine Learning. In spite of their success, they still find limited application in safety- and security-related contexts, wherein assurance about networksu0027 performances must be provided. In the recent past, automated reasoning techniques have been proposed by several researchers to close the gap between neural networks and applications requiring formal guarantees about their behavior. In this work, we propose a primer of such techniques and a comprehensive categorization of existing approaches for the automated verification of neural networks. A discussion about current limitations and directions for future investigation is provided to foster research on this topic at the crossroads of Machine Learning and Automated Reasoning. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Artificial Intelligence | Automated reasoning,Categorization,Computer science,Artificial intelligence,Artificial neural network,Machine learning,Spite |
DocType | Volume | Citations |
Journal | abs/1805.09938 | 3 |
PageRank | References | Authors |
0.37 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francesco Leofante | 1 | 16 | 5.71 |
| Nina Narodytska | 2 | 449 | 39.28 |
| Luca Pulina | 3 | 326 | 37.95 |
| Armando Tacchella | 4 | 1448 | 108.82 |