Davide Bacciu

2.7k total citations
123 papers, 1.2k citations indexed

About

Davide Bacciu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Statistical and Nonlinear Physics. According to data from OpenAlex, Davide Bacciu has authored 123 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 85 papers in Artificial Intelligence, 31 papers in Computer Vision and Pattern Recognition and 14 papers in Statistical and Nonlinear Physics. Recurrent topics in Davide Bacciu's work include Advanced Graph Neural Networks (17 papers), Neural Networks and Reservoir Computing (16 papers) and Neural Networks and Applications (15 papers). Davide Bacciu is often cited by papers focused on Advanced Graph Neural Networks (17 papers), Neural Networks and Reservoir Computing (16 papers) and Neural Networks and Applications (15 papers). Davide Bacciu collaborates with scholars based in Italy, United Kingdom and Spain. Davide Bacciu's co-authors include Alessio Micheli, Marco Podda, Federico Errica, Antonio Carta, Andrea Cossu, Claudio Gallicchio, Vincenzo Lomonaco, Stefano Chessa, Alessandro Sperduti and Paolo Barsocchi and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Davide Bacciu

114 papers receiving 1.1k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Davide Bacciu Italy 16 632 247 169 131 116 123 1.2k
Jin Li China 20 528 0.8× 343 1.4× 477 2.8× 154 1.2× 160 1.4× 130 1.6k
Mineichi Kudo Japan 18 734 1.2× 611 2.5× 131 0.8× 121 0.9× 109 0.9× 111 1.7k
Christian Napoli Italy 19 523 0.8× 177 0.7× 416 2.5× 145 1.1× 111 1.0× 128 1.4k
Amany Sarhan Egypt 15 415 0.7× 479 1.9× 167 1.0× 78 0.6× 184 1.6× 96 1.4k
Kyunghyun Cho United States 16 973 1.5× 606 2.5× 113 0.7× 84 0.6× 61 0.5× 39 1.5k
Oh-Young Song South Korea 19 460 0.7× 369 1.5× 115 0.7× 69 0.5× 295 2.5× 71 1.6k
Jiejun Xu United States 17 773 1.2× 432 1.7× 102 0.6× 71 0.5× 129 1.1× 48 1.6k
Çaǧlar Gülçehre Canada 11 1.0k 1.6× 526 2.1× 142 0.8× 81 0.6× 65 0.6× 26 1.7k
Zongqing Lu China 19 259 0.4× 261 1.1× 200 1.2× 105 0.8× 391 3.4× 77 1.1k
Arash Sharifi Iran 18 345 0.5× 136 0.6× 128 0.8× 104 0.8× 131 1.1× 80 902

Countries citing papers authored by Davide Bacciu

Since Specialization
Citations

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

Fields of papers citing papers by Davide Bacciu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Davide Bacciu

This figure shows the co-authorship network connecting the top 25 collaborators of Davide Bacciu. A scholar is included among the top collaborators of Davide Bacciu 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 Davide Bacciu. Davide Bacciu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Podda, Marco, et al.. (2025). Towards Efficient Molecular Property Optimization with Graph Energy Based Models. CINECA IRIS Institutial research information system (University of Pisa). 289–294. 1 indexed citations
2.
Cossu, Andrea, et al.. (2025). Don't drift away: Advances and Applications of Streaming and Continual Learning. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 35–44.
3.
Navarin, Nicolò, et al.. (2025). Informed machine learning for complex data. Neurocomputing. 669. 132505–132505.
4.
Cossu, Andrea, et al.. (2024). Continual pre-training mitigates forgetting in language and vision. Neural Networks. 179. 106492–106492. 7 indexed citations
5.
Gallicchio, Claudio, et al.. (2023). Continual adaptation of federated reservoirs in pervasive environments. Neurocomputing. 556. 126638–126638. 4 indexed citations
6.
Bacciu, Davide, Federico Errica, Alessio Micheli, et al.. (2023). Graph Representation Learning. CINECA IRIS Institutial research information system (University of Pisa). 1–10. 1 indexed citations
7.
Cucinotta, Tommaso, et al.. (2023). A 2-Phase Strategy for Intelligent Cloud Operations. IEEE Access. 11. 96841–96853. 1 indexed citations
8.
Bacciu, Davide, et al.. (2023). Generalizing Downsampling from Regular Data to Graphs. Proceedings of the AAAI Conference on Artificial Intelligence. 37(6). 6718–6727. 2 indexed citations
9.
Carta, Antonio, Andrea Cossu, Vincenzo Lomonaco, & Davide Bacciu. (2022). Ex-Model: Continual Learning from a Stream of Trained Models. CINECA IRIS Institutial research information system (University of Pisa). 8 indexed citations
10.
Melis, Marco, et al.. (2022). FADER: Fast adversarial example rejection. CINECA IRIS Institutial research information system (University of Pisa). 9 indexed citations
11.
Barsotti, Michele, et al.. (2020). ROS-Neuro Integration of Deep Convolutional Autoencoders for EEG Signal Compression in Real-time BCIs. CINECA IRIS Institutial research information system (University of Pisa). 5 indexed citations
12.
Bacciu, Davide, et al.. (2020). Generalising Recursive Neural Models by Tensor Decomposition. CINECA IRIS Institutial research information system (University of Pisa). 1 indexed citations
13.
Bacciu, Davide, et al.. (2019). Bayesian Tensor Factorisation for Bottom-up Hidden Tree Markov Models. CINECA IRIS Institutial research information system (University of Pisa). 3 indexed citations
14.
Santina, Cosimo Della, Giuseppe Averta, Alessandro Settimi, et al.. (2019). Learning From Humans How to Grasp: A Data-Driven Architecture for Autonomous Grasping With Anthropomorphic Soft Hands. IEEE Robotics and Automation Letters. 4(2). 1533–1540. 62 indexed citations
15.
Bacciu, Davide, Michele Colombo, Davide Morelli, & David Plans. (2018). Randomized neural networks for preference learning with physiological data. Neurocomputing. 298. 9–20. 10 indexed citations
16.
Bacciu, Davide, Alessio Micheli, & Alessandro Sperduti. (2018). Generative Kernels for Tree-Structured Data. IEEE Transactions on Neural Networks and Learning Systems. 29(10). 4932–4946. 11 indexed citations
17.
Bacciu, Davide, Michele Colombo, Davide Morelli, & David Plans. (2017). ELM Preference Learning for Physiological Data. The European Symposium on Artificial Neural Networks. 99–104. 2 indexed citations
18.
Bacciu, Davide, et al.. (2015). ESNigma: efficient feature selection for Echo State Networks. CINECA IRIS Institutial research information system (University of Pisa). 189–194. 5 indexed citations
19.
Bacciu, Davide, et al.. (2009). Adaptive Service Selection - A Fuzzy-valued Matchmaking Approach. UnipiEprints Open Archive (Università di Pisa). 2 indexed citations
20.
Bacciu, Davide, Alessio Botta, & Dan Ştefănescu. (2007). A framework for semantic querying of distributed data-graphs via information granules. CINECA IRIS Institutial research information system (University of Pisa). 3 indexed citations

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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