Alessio Micheli
- Artificial Intelligence top 0.5%
- Neural Networks and Reservoir Computing 56
- Neural Networks and Applications 44
- Advanced Graph Neural Networks 14
- Machine Learning and ELM 7
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- Computational Drug Discovery Methods 24
- Signal Processing top 5%
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- Advanced Memory and Neural Computing 39
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- Machine Learning in Materials Science 14
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- Neural dynamics and brain function 12
Alessio Micheli
139 papers receiving 2.9k citations
Hit Papers
Peers
Comparison fields: 5 of 157
- Artificial Intelligence 2.0k
- Computer Vision and Pattern Recognition 482
- Statistical and Nonlinear Physics 258
- Computational Theory and Mathematics 287
- Signal Processing 189
Countries citing papers authored by Alessio Micheli
This map shows the geographic impact of Alessio Micheli'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 Alessio Micheli with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alessio Micheli more than expected).
Fields of papers citing papers by Alessio Micheli
This network shows the impact of papers produced by Alessio Micheli. 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 Alessio Micheli. The network helps show where Alessio Micheli may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Alessio Micheli, 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 | 2024 | 1 | |
| 2 | 2024 | 0 | |
| 3 | 2023 | 1 | |
| 4 | 2023 | 2 | |
| 5 | 2023 | 2 | |
| 6 | 2022 | 6 | |
| 7 | 2021 | 3 | |
| 8 | 2020 | 8 | |
| 9 | A Deep Generative Model for Fragment-Based Molecule Generation. | 2020 | 4 |
| 10 | Probabilistic Learning on Graphs via Contextual Architectures | 2020 | 5 |
| 11 | Graph generation by sequential edge prediction. | 2019 | 4 |
| 12 | Pyramidal Graph Echo State Networks | 2018 | 1 |
| 13 | 2018 | 11 | |
| 14 | RSS-based Robot Localization in Critical Environments using Reservoir Computing | 2016 | 4 |
| 15 | A Reservoir Computing Approach for Human Gesture Recognition from Kinect Data. | 2016 | 3 |
| 16 | ESNigma: efficient feature selection for Echo State Networks | 2015 | 5 |
| 17 | Input-Output Hidden Markov Models for Trees | 2012 | 1 |
| 18 | Reservoir Computing Forecasting of User Movements from RSS Mote-Class Sensors Measurement | 2011 | 2 |
| 19 | A Comparative Study of Tree Generative Kernels for Gene Function Prediction | 2007 | 1 |
| 20 | A Note on Formal Determination of Context in Contextual Recursive Cascade Correlation Networks | 2004 | 1 |
About Alessio Micheli
Alessio Micheli is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Signal Processing, having authored 148 papers that have together received 3.0k indexed citations. Recurring topics across this work include Neural Networks and Reservoir Computing (56 papers), Neural Networks and Applications (44 papers), Advanced Memory and Neural Computing (39 papers), Computational Drug Discovery Methods (24 papers), Advanced Graph Neural Networks (14 papers), Machine Learning in Materials Science (14 papers), Neural dynamics and brain function (12 papers) and Machine Learning and ELM (7 papers). The work is most often cited by research in Artificial Intelligence (2.0k citations), Computer Vision and Pattern Recognition (482 citations) and Statistical and Nonlinear Physics (258 citations). Alessio Micheli has collaborated with scholars based in Italy, Germany and Sweden. Frequent co-authors include Claudio Gallicchio, Luca Pedrelli, Davide Bacciu, Alessandro Sperduti, Marco Podda, Federico Errica, Antonina Starita, Barbara Hammer, Maria Rosaria Tinè and Celia Duce. Their work appears in journals such as Bioinformatics, PLoS ONE and Scientific Reports.
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.