Ambra Demontis
- Artificial Intelligence top 2%
- Signal Processing top 5%
- Computer Networks and Communications top 10%
- Information Systems top 10%
- Computer Vision and Pattern Recognition
- Co-authors
- Battista BiggioFabio RoliAndrea PaudiceLuis Muñoz-GonzálezEmil LupuGiorgio FumeraMaura PintorMarco Melis
- Topics
- Adversarial Robustness in Machine Learning (22 papers)Anomaly Detection Techniques and Applications (12 papers)Advanced Malware Detection Techniques (9 papers)
- Partner nations
- ItalyChinaUnited Kingdom
In The Last Decade
Ambra Demontis
22 papers receiving 558 citations
Hit Papers
Peers
Comparison fields: 5 of 64
- Artificial Intelligence 484
- Signal Processing 203
- Computer Networks and Communications 115
- Information Systems 60
- Computer Vision and Pattern Recognition 55
Countries citing papers authored by Ambra Demontis
This map shows the geographic impact of Ambra Demontis's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ambra Demontis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ambra Demontis more than expected).
Fields of papers citing papers by Ambra Demontis
This network shows the impact of papers produced by Ambra Demontis. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ambra Demontis. The network helps show where Ambra Demontis may publish in the future.
Co-authorship network of co-authors of Ambra Demontis
This figure shows the co-authorship network connecting the top 25 collaborators of Ambra Demontis. A scholar is included among the top collaborators of Ambra Demontis based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ambra Demontis. Ambra Demontis is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 5 | |
| 4 | 12 | |
| 5 | 2 | |
| 6 | 0 | |
| 7 | 2 | |
| 8 | 1 | |
| 9 | 8 | |
| 10 | 58 | |
| 11 | 8 | |
| 12 | 28 | |
| 13 | 12 | |
| 14 | 2 | |
| 15 | 21 | |
| 16 | 11 | |
| 17 | 44 | |
| 18 | On the Intriguing Connections of Regularization, Input Gradients and Transferability of Evasion and Poisoning Attacks. | 2 |
| 19 | Towards poisoning of deep learning algorithms with back-gradient optimizationbreakdown → | 287 |
| 20 | 29 |
About Ambra Demontis
Ambra Demontis is a scholar working on Signal Processing, Artificial Intelligence and Hardware and Architecture, having authored 25 papers that have together received 587 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (22 papers), Anomaly Detection Techniques and Applications (12 papers) and Advanced Malware Detection Techniques (9 papers). The work is most often cited by research in Signal Processing (203 citations), Artificial Intelligence (484 citations) and Health Informatics (7 citations). Ambra Demontis has collaborated with scholars based in Italy, China and United Kingdom. Frequent co-authors include Battista Biggio, Fabio Roli, Andrea Paudice, Luis Muñoz-González, Emil Lupu, Giorgio Fumera, Maura Pintor, Marco Melis, Marcello Pelillo and Kathrin Grosse. Their work appears in journals such as ACM Computing Surveys, Pattern Recognition and Information Sciences.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.