M. Re

168 papers receiving 1.7k citations

M. Re's Hit Papers

Multi-Agent Reinforcement Learning: A Review of Challenges and Applications 2021 · 218 citations
2180+1+3Years since publication50100150200

Peers

M. Re
Comparison fields: 5 of 107
  • Hardware and Architecture 293
  • Artificial Intelligence 634
  • Computational Theory and Mathematics 306
  • Information Systems 410
  • Signal Processing 177
Replace G.C. Cardarilli with:
G.C. Cardarilli Italy
Ulrich Rückert Germany
Fayez Gebali Canada
Xicheng Lu China
Paolo Montuschi Italy
Sofiène Tahar Canada
Nobuo Funabiki Japan
Jonathan Bachrach United States
Wai‐Kong Lee Malaysia
Hongbin Sun China
M. Re relative to G.C. Cardarilli Italy G.C. Cardarilli's profile →
Citations per field
00.5×1.5×
G.C. Cardarilli · 1×
Citations per year

Countries citing papers authored by M. Re

Since Specialization
Citations

This map shows the geographic impact of M. Re'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. Re with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. Re more than expected).

Fields of papers citing papers by M. Re

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by M. Re. 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. Re. The network helps show where M. Re may publish in the future.

Co-authors

The 25 scholars most cited alongside M. Re, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with M. Re Line = papers co-authored together M. Re links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 177 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Multi-Agent Reinforcement Learning: A Review of Challenges and Applications
Hit paper breakdown →
2021218
2 201981
3 200770
4 202167
5 202056
6 200746
7 200240
8 200340
9 201239
10 200535
11 201733
12 200231
13 201926
14 200826
15 201926
16 201924
17 199823
18 201921
19 200621
20 200720

About M. Re

M. Re is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence, Hardware and Architecture, Computational Theory and Mathematics and Information Systems, having authored 177 papers that have together received 1.8k indexed citations. Recurring topics across this work include Cryptography and Residue Arithmetic (38 papers), Numerical Methods and Algorithms (37 papers), Parallel Computing and Optimization Techniques (31 papers), Low-power high-performance VLSI design (27 papers), Embedded Systems Design Techniques (25 papers), Digital Filter Design and Implementation (21 papers), Cryptographic Implementations and Security (21 papers) and Coding theory and cryptography (19 papers). The work is most often cited by research in Hardware and Architecture (293 citations), Artificial Intelligence (634 citations), Computational Theory and Mathematics (306 citations), Information Systems (410 citations) and Signal Processing (177 citations). M. Re has collaborated with scholars based in Italy, Denmark and United States. Frequent co-authors include G.C. Cardarilli, Alberto Nannarelli, Luca Di Nunzio, Rocco Fazzolari, Sergio Spanò, Daniele Giardino, Salvatore Pontarelli, A. Salsano, Lorenzo Canese and Marco Ottavi. Their work appears in journals such as IEEE Access, IEEE Transactions on Circuits & Systems II Express Briefs, Applied Sciences, Scientific Reports and Electronics.

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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