Stefano Spigler

440 total citations
8 papers, 181 citations indexed

About

Stefano Spigler is a scholar working on Condensed Matter Physics, Materials Chemistry and Artificial Intelligence. According to data from OpenAlex, Stefano Spigler has authored 8 papers receiving a total of 181 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Condensed Matter Physics, 4 papers in Materials Chemistry and 3 papers in Artificial Intelligence. Recurrent topics in Stefano Spigler's work include Material Dynamics and Properties (4 papers), Theoretical and Computational Physics (4 papers) and Neural Networks and Applications (3 papers). Stefano Spigler is often cited by papers focused on Material Dynamics and Properties (4 papers), Theoretical and Computational Physics (4 papers) and Neural Networks and Applications (3 papers). Stefano Spigler collaborates with scholars based in France, Switzerland and United Kingdom. Stefano Spigler's 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 and has published in prestigious journals such as Physical review. E, Journal of Statistical Mechanics Theory and Experiment and Machine Learning Science and Technology.

In The Last Decade

Stefano Spigler

8 papers receiving 171 citations

Peers

Stefano Spigler

AI

MC

CMP

SNP

CVPR

Antoine Gerschenfeld France

Lasse Bjørn Kristensen Denmark

Sirui Lu China

Punyashloka Debashis United States

Zhengtao Yu China

Xueyuan Hu China

Ruo-Jing Ren China

Takashi Shibuya Japan

Tailin Wu United States

I. Grosu Romania

Antoine Gerschenfeld France

Stefano Spigler

18 ×0.2

AI

63 ×1.3

MC

75 ×1.6

CMP

65 ×1.7

SNP

3 ×0.1

CVPR

Citations per year, relative to Stefano Spigler Stefano Spigler (= 1×) peers Antoine Gerschenfeld

Countries citing papers authored by Stefano Spigler

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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8 of 8 papers shown

1.

Geiger, Mario, Arthur Paul Jacot, Stefano Spigler, et al.. (2020). Scaling description of generalization with number of parameters in deep learning. Journal of Statistical Mechanics Theory and Experiment. 2020(2). 23401–23401. 67 indexed citations

2.

Spigler, Stefano, et al.. (2020). How isotropic kernels perform on simple invariants. Machine Learning Science and Technology. 2(2). 25020–25020. 5 indexed citations

3.

Geiger, Mario, Stefano Spigler, Stéphane d’Ascoli, et al.. (2019). Jamming transition as a paradigm to understand the loss landscape of deep neural networks. Physical review. E. 100(1). 12115–12115. 43 indexed citations

4.

Geiger, Mario, Stefano Spigler, Arthur Paul Jacot, & Matthieu Wyart. (2019). Disentangling feature and lazy learning in deep neural networks: an empirical study.. arXiv (Cornell University). 3 indexed citations

5.

Baity‐Jesi, Marco, Levent Sagun, Mario Geiger, et al.. (2018). Comparing dynamics: deep neural networks versus glassy systems. IRIS Research product catalog (Sapienza University of Rome). 22 indexed citations

6.

Baity‐Jesi, Marco, Levent Sagun, Mario Geiger, et al.. (2018). Comparing Dynamics: Deep Neural Networks versus Glassy Systems. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 80. 314–323. 7 indexed citations

7.

Franz, Silvio & Stefano Spigler. (2017). Mean-field avalanches in jammed spheres. Physical review. E. 95(2). 22139–22139. 32 indexed citations

8.

Franz, Silvio, Giacomo Gradenigo, & Stefano Spigler. (2016). Random-diluted triangular plaquette model: Study of phase transitions in a kinetically constrained model. Physical review. E. 93(3). 32601–32601. 2 indexed citations

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.

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