M. Paganini
- Nuclear and High Energy Physics top 5%
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Statistical and Nonlinear Physics top 10%
- Radiology, Nuclear Medicine and Imaging
- Co-authors
- Luke de OliveiraBenjamin NachmanYuandong TianAri S. MorcosHaonan YuW. LegrosAndré NicoletJ. W. Smith
- Topics
- Particle physics theoretical and experimental studies (3 papers)High-Energy Particle Collisions Research (3 papers)Astrophysics and Cosmic Phenomena (3 papers)
- Cited by
- Nuclear and High Energy PhysicsArtificial IntelligenceComputer Vision and Pattern Recognition
- Partner nations
- United StatesSwitzerlandBelgium
In The Last Decade
M. Paganini
7 papers receiving 419 citations
Hit Papers
Peers
Comparison fields: 5 of 61
- Nuclear and High Energy Physics 269
- Artificial Intelligence 140
- Computer Vision and Pattern Recognition 82
- Statistical and Nonlinear Physics 41
- Radiology, Nuclear Medicine and Imaging 31
Countries citing papers authored by M. Paganini
This map shows the geographic impact of M. Paganini'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 M. Paganini with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. Paganini more than expected).
Fields of papers citing papers by M. Paganini
This network shows the impact of papers produced by M. Paganini. 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 M. Paganini. The network helps show where M. Paganini may publish in the future.
Co-authorship network of co-authors of M. Paganini
This figure shows the co-authorship network connecting the top 25 collaborators of M. Paganini. A scholar is included among the top collaborators of M. Paganini 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 M. Paganini. M. Paganini is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers | 17 |
| 2 | 0 | |
| 3 | 21 | |
| 4 | 142 | |
| 5 | CaloGAN: Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networksbreakdown → | 195 |
| 6 | 44 | |
| 7 | 0 | |
| 8 | 2 | |
| 9 | 3 |
About M. Paganini
M. Paganini is a scholar working on Nuclear and High Energy Physics, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 9 papers that have together received 424 indexed citations. Recurring topics across this work include Particle physics theoretical and experimental studies (3 papers), High-Energy Particle Collisions Research (3 papers) and Astrophysics and Cosmic Phenomena (3 papers). The work is most often cited by research in Nuclear and High Energy Physics (269 citations), Artificial Intelligence (140 citations) and Computer Vision and Pattern Recognition (82 citations). M. Paganini has collaborated with scholars based in United States, Switzerland and Belgium. Frequent co-authors include Luke de Oliveira, Benjamin Nachman, Yuandong Tian, Ari S. Morcos, Haonan Yu, W. Legros, André Nicolet, J. W. Smith, A. Krasznahorkay and M. Kagan. Their work appears in journals such as Physical Review Letters, IEEE Transactions on Magnetics and Physical review. D.
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.