Marcelo Blatt

1.0k citations
12 papers · 666 · h-index 8

Impact in

Papers in

    • Neural Networks and Applications 7
    • Advanced Clustering Algorithms Research 2
    • Cryptography and Data Security 2
    • Privacy-Preserving Technologies in Data 2
    • Blind Source Separation Techniques 4

Marcelo Blatt

11 papers receiving 624 citations

Peers

Marcelo Blatt
Comparison fields: 5 of 101
  • Statistical and Nonlinear Physics 110
  • Artificial Intelligence 253
  • Signal Processing 58
  • Cognitive Neuroscience 90
  • Computer Vision and Pattern Recognition 85
Replace Carlo Baldassi with:
Carlo Baldassi Italy
Srinandan Dasmahapatra United Kingdom
Shai Wiseman Israel
Juan A. Sigüenza Spain
Amin Vahdat United States
Kristina Lisa Klinkner United States
Bruno Apolloni Italy
Zoran Levnajić Slovenia
Renato M. Capocelli Italy
Elena Agliari Italy
Marcelo Blatt relative to Carlo Baldassi Italy Carlo Baldassi's profile →
Citations per field
00.5×1.5×
Carlo Baldassi · 1×
Citations per year

Countries citing papers authored by Marcelo Blatt

Since Specialization
Citations

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

Fields of papers citing papers by Marcelo Blatt

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 17 scholars most cited alongside Marcelo Blatt, 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 Marcelo Blatt Line = papers co-authored together Marcelo Blatt links everyone, so they are left out of the graph.

All Works

12 of 12 papers shown
#Work
1 1996363
2 1997128
3 202069
4 199832
5 202023
6 199419
7
Clustering data through an analogy to the Potts model
199515
8 19998
9 19915
10 19953
11 19961
12 20210

About Marcelo Blatt

Marcelo Blatt is a scholar working on Artificial Intelligence, Signal Processing, Condensed Matter Physics, Computational Theory and Mathematics and Pharmacy, having authored 12 papers that have together received 666 indexed citations. Recurring topics across this work include Neural Networks and Applications (7 papers), Blind Source Separation Techniques (4 papers), Theoretical and Computational Physics (4 papers), Oral and gingival health research (2 papers), Advanced Clustering Algorithms Research (2 papers), Cryptography and Data Security (2 papers), Privacy-Preserving Technologies in Data (2 papers) and Complex Network Analysis Techniques (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (110 citations), Artificial Intelligence (253 citations), Signal Processing (58 citations), Cognitive Neuroscience (90 citations) and Computer Vision and Pattern Recognition (85 citations). Marcelo Blatt has collaborated with scholars based in Israel, United States and Argentina. Frequent co-authors include Eytan Domany, Shai Wiseman, Alexander Gusev, Yuriy Polyakov, Shafi Goldwasser, Ido Kanter, Vinod Vaikuntanathan, Kurt Rohloff, Avner Priel and E. Vergini. Their work appears in journals such as Physical Review Letters, Computer Physics Communications, Journal of Clinical Oncology, Neural Computation and International Journal of Neural Systems.

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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