Andre Manoel
Impact in
Papers in
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- Neural Networks and Applications 3
- Privacy-Preserving Technologies in Data 2
- Stochastic Gradient Optimization Techniques 2
- Domain Adaptation and Few-Shot Learning 1
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- Blind Source Separation Techniques 3
- Co-authors
- Florent Krząkała (5 shared papers)Lenka Zdeborová (4 shared papers)Robert B. Sim (2 shared papers)Yae Jee Cho (1 shared paper)Dimitrios Dimitriadis (1 shared paper)Jean Barbier (2 shared papers)Nicolas Macris (1 shared paper)Eric W. Tramel (2 shared papers)
- Journals
- Diabetes Research and Clinical Practice (1 paper)Journal of Statistical Mechanics Theory and Experiment (2 papers)DOAJ (DOAJ: Directory of Open Access Journals) (1 paper)HAL (Le Centre pour la Communication Scientifique Directe) (1 paper)Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (1 paper)
- Partner nations
- FranceBrazilSwitzerland
In The Last Decade
Andre Manoel
10 papers receiving 234 citations
Peers
Comparison fields: 5 of 68
- Computational Mathematics 3
- Health Informatics 6
- Artificial Intelligence 135
- Signal Processing 30
- Acoustics and Ultrasonics 2
Countries citing papers authored by Andre Manoel
This map shows the geographic impact of Andre Manoel'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 Andre Manoel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andre Manoel more than expected).
Fields of papers citing papers by Andre Manoel
This network shows the impact of papers produced by Andre Manoel. 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 Andre Manoel. The network helps show where Andre Manoel may publish in the future.
Co-authors
The 25 scholars most cited alongside Andre Manoel, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 71 | |
| 2 | 2022 | 61 | |
| 3 | 2022 | 25 | |
| 4 | Swept Approximate Message Passing for Sparse Estimation | 2015 | 21 |
| 5 | 2024 | 17 | |
| 6 | Multi-Layer Generalized Linear Estimation | 2017 | 15 |
| 7 | 2024 | 14 | |
| 8 | 2016 | 9 | |
| 9 | 2018 | 6 | |
| 10 | 2013 | 2 |
About Andre Manoel
Andre Manoel is a scholar working on Artificial Intelligence, Signal Processing, Statistical and Nonlinear Physics, Computational Mechanics and Computer Networks and Communications, having authored 10 papers that have together received 241 indexed citations. Recurring topics across this work include Neural Networks and Applications (3 papers), Sparse and Compressive Sensing Techniques (3 papers), Blind Source Separation Techniques (3 papers), Statistical Mechanics and Entropy (2 papers), Neural dynamics and brain function (2 papers), Privacy-Preserving Technologies in Data (2 papers), Stochastic Gradient Optimization Techniques (2 papers) and Domain Adaptation and Few-Shot Learning (1 paper). The work is most often cited by research in Computational Mathematics (3 citations), Health Informatics (6 citations), Artificial Intelligence (135 citations), Signal Processing (30 citations) and Acoustics and Ultrasonics (2 citations). Andre Manoel has collaborated with scholars based in France, Brazil and Switzerland. Frequent co-authors include Florent Krząkała, Lenka Zdeborová, Robert B. Sim, Yae Jee Cho, Dimitrios Dimitriadis, Jean Barbier, Nicolas Macris, Eric W. Tramel, Marc Mézard and Fernando Yue Cesena. Their work appears in journals such as Diabetes Research and Clinical Practice, Journal of Statistical Mechanics Theory and Experiment, DOAJ (DOAJ: Directory of Open Access Journals), HAL (Le Centre pour la Communication Scientifique Directe) and Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence.
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.