Stefano Spigler
- Artificial Intelligence top 10%
- Materials Chemistry
- Condensed Matter Physics
- Statistical and Nonlinear Physics top 10%
- Computer Vision and Pattern Recognition
- Co-authors
- Matthieu WyartMario GeigerGiulio BiroliLevent SagunSilvio FranzStéphane d’AscoliMarco Baity‐JesiArthur Paul Jacot
- Topics
- Material Dynamics and Properties (4 papers)Theoretical and Computational Physics (4 papers)Neural Networks and Applications (3 papers)
- Journals
- Physical review. EJournal of Statistical Mechanics Theory and ExperimentMachine Learning Science and Technology
- Partner nations
- FranceSwitzerlandUnited Kingdom
In The Last Decade
Stefano Spigler
8 papers receiving 171 citations
Peers
Comparison fields: 5 of 52
- Artificial Intelligence 98
- Materials Chemistry 49
- Condensed Matter Physics 46
- Statistical and Nonlinear Physics 39
- Computer Vision and Pattern Recognition 24
Countries citing papers authored by Stefano Spigler
This map shows the geographic impact of Stefano Spigler'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 Stefano Spigler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stefano Spigler more than expected).
Fields of papers citing papers by Stefano Spigler
This network shows the impact of papers produced by Stefano Spigler. 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 Stefano Spigler. The network helps show where Stefano Spigler may publish in the future.
Co-authorship network of co-authors of Stefano Spigler
This figure shows the co-authorship network connecting the top 25 collaborators of Stefano Spigler. A scholar is included among the top collaborators of Stefano Spigler 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 Stefano Spigler. Stefano Spigler is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 67 | |
| 2 | 5 | |
| 3 | 43 | |
| 4 | Disentangling feature and lazy learning in deep neural networks: an empirical study. | 3 |
| 5 | Comparing dynamics: deep neural networks versus glassy systems | 22 |
| 6 | 7 | |
| 7 | 32 | |
| 8 | 2 |
About Stefano Spigler
Stefano Spigler is a scholar working on Condensed Matter Physics, Statistical and Nonlinear Physics and Artificial Intelligence, having authored 8 papers that have together received 181 indexed citations. Recurring topics across this work include Material Dynamics and Properties (4 papers), Theoretical and Computational Physics (4 papers) and Neural Networks and Applications (3 papers). The work is most often cited by research in Computational Mathematics (3 citations), Condensed Matter Physics (46 citations) and Statistical and Nonlinear Physics (39 citations). Stefano Spigler has collaborated with scholars based in France, Switzerland and United Kingdom. Frequent co-authors include Matthieu Wyart, Mario Geiger, Giulio Biroli, Levent Sagun, Silvio Franz, Stéphane d’Ascoli, Marco Baity‐Jesi, Arthur Paul Jacot, Clément Hongler and Yann LeCun. Their work appears in journals such as Physical review. E, Journal of Statistical Mechanics Theory and Experiment and Machine Learning Science and Technology.
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.