Antonio Carta

641 total citations
19 papers, 201 citations indexed

About

Antonio Carta is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Automotive Engineering. According to data from OpenAlex, Antonio Carta has authored 19 papers receiving a total of 201 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 7 papers in Computer Vision and Pattern Recognition and 2 papers in Automotive Engineering. Recurrent topics in Antonio Carta's work include Domain Adaptation and Few-Shot Learning (8 papers), Multimodal Machine Learning Applications (6 papers) and Data Stream Mining Techniques (3 papers). Antonio Carta is often cited by papers focused on Domain Adaptation and Few-Shot Learning (8 papers), Multimodal Machine Learning Applications (6 papers) and Data Stream Mining Techniques (3 papers). Antonio Carta collaborates with scholars based in Italy, Greece and Switzerland. Antonio Carta's co-authors include Davide Bacciu, Andrea Cossu, Vincenzo Lomonaco, Laura Semini, Stefania Gnesi, Joost van de Weijer, Claudio Gallicchio, Davide Maltoni, Gabriele Graffieti and Lorenzo Pellegrini and has published in prestigious journals such as Neurocomputing, Neural Networks and Frontiers in Artificial Intelligence.

In The Last Decade

Antonio Carta

18 papers receiving 199 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Antonio Carta Italy 8 129 50 22 17 15 19 201
Andrea Cossu Italy 7 128 1.0× 52 1.0× 22 1.0× 6 0.4× 16 1.1× 15 178
Tuong Do United Kingdom 6 125 1.0× 105 2.1× 28 1.3× 7 0.4× 19 1.3× 16 202
Yuntao Du China 5 113 0.9× 43 0.9× 27 1.2× 28 1.6× 19 1.3× 14 224
Shukang Yin China 4 97 0.8× 61 1.2× 7 0.3× 10 0.6× 8 0.5× 6 218
Haekyu Park United States 4 80 0.6× 72 1.4× 10 0.5× 7 0.4× 5 0.3× 6 198
Congbo Ma Australia 6 105 0.8× 67 1.3× 13 0.6× 5 0.3× 12 0.8× 11 213
Junfeng Hu China 7 77 0.6× 17 0.3× 11 0.5× 17 1.0× 9 0.6× 31 178
Fernando E. Casado Spain 7 115 0.9× 34 0.7× 22 1.0× 6 0.4× 8 0.5× 10 173
Ürün Doǧan United States 8 120 0.9× 68 1.4× 11 0.5× 15 0.9× 29 1.9× 14 190
Pınar Kirci Türkiye 8 120 0.9× 21 0.4× 39 1.8× 12 0.7× 22 1.5× 53 281

Countries citing papers authored by Antonio Carta

Since Specialization
Citations

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

Fields of papers citing papers by Antonio Carta

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Antonio Carta

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

All Works

19 of 19 papers shown
1.
Cossu, Andrea, et al.. (2024). Continual pre-training mitigates forgetting in language and vision. Neural Networks. 179. 106492–106492. 7 indexed citations
2.
Carta, Antonio, et al.. (2024). Updating knowledge in Large Language Models: an Empirical Evaluation. CINECA IRIS Institutial research information system (University of Pisa). 1–8. 1 indexed citations
3.
Carta, Antonio, Andrea Cossu, Vincenzo Lomonaco, Davide Bacciu, & Joost van de Weijer. (2024). Projected Latent Distillation for Data-Agnostic Consolidation in distributed continual learning. Neurocomputing. 598. 127935–127935. 3 indexed citations
4.
Carta, Antonio, et al.. (2023). A Simple Recipe to Meta-Learn Forward and Backward Transfer. CINECA IRIS Institutial research information system (University of Pisa). 33. 18686–18696. 1 indexed citations
5.
Lomonaco, Vincenzo, Claudio Gallicchio, Antonio Carta, et al.. (2023). AI-Toolkit: A Microservices Architecture for Low-Code Decentralized Machine Intelligence. CINECA IRIS Institutial research information system (University of Pisa). 1–5.
6.
Carta, Antonio, et al.. (2023). A Comprehensive Empirical Evaluation on Online Continual Learning. CINECA IRIS Institutial research information system (University of Pisa). 3510–3520. 9 indexed citations
7.
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
8.
Carta, Antonio, Andrea Cossu, Federico Errica, & Davide Bacciu. (2022). Catastrophic Forgetting in Deep Graph Networks: A Graph Classification Benchmark. Frontiers in Artificial Intelligence. 5. 824655–824655. 4 indexed citations
9.
Cossu, Andrea, Gabriele Graffieti, Lorenzo Pellegrini, et al.. (2022). Is Class-Incremental Enough for Continual Learning?. Frontiers in Artificial Intelligence. 5. 829842–829842. 15 indexed citations
10.
Carta, Antonio, et al.. (2022). Sample Condensation in Online Continual Learning. 2022 International Joint Conference on Neural Networks (IJCNN). 1–8. 19 indexed citations
11.
Gotta, Alberto, Pietro Cassará, Antonio Carta, et al.. (2022). AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving. CINECA IRIS Institutial research information system (University of Pisa). 6 indexed citations
12.
Cossu, Andrea, Antonio Carta, Vincenzo Lomonaco, & Davide Bacciu. (2021). Continual learning for recurrent neural networks: An empirical evaluation. Neural Networks. 143. 607–627. 85 indexed citations
13.
Cossu, Andrea, Davide Bacciu, Antonio Carta, Claudio Gallicchio, & Vincenzo Lomonaco. (2021). Continual Learning with Echo State Networks. CINECA IRIS Institutial research information system (University of Pisa). 275–280. 6 indexed citations
14.
Carta, Antonio, Alessandro Sperduti, & Davide Bacciu. (2021). Encoding-based memory for recurrent neural networks. Neurocomputing. 456. 407–420. 3 indexed citations
15.
Bacciu, Davide, et al.. (2021). Towards Functional Safety Compliance of Recurrent Neural Networks. CINECA IRIS Institutial research information system (University of Pisa). 1 indexed citations
16.
Cossu, Andrea, Antonio Carta, & Davide Bacciu. (2020). Continual Learning with Gated Incremental Memories for sequential data processing. arXiv (Cornell University). 1–8. 12 indexed citations
17.
Florio, A. Di, F. Pantaleo, & Antonio Carta. (2018). Convolutional Neural Network for Track Seed Filtering at the CMS High-Level Trigger. Journal of Physics Conference Series. 1085. 42040–42040. 2 indexed citations
18.
Attardi, Giuseppe, et al.. (2017). FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering. CINECA IRIS Institutial research information system (University of Pisa). 299–304. 2 indexed citations
19.
Bacciu, Davide, Antonio Carta, Stefania Gnesi, & Laura Semini. (2016). An experience in using machine learning for short-term predictions in smart transportation systems. Journal of Logical and Algebraic Methods in Programming. 87. 52–66. 17 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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