Simone Scardapane

5.0k total citations · 2 hit papers
96 papers, 2.7k citations indexed

About

Simone Scardapane is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Simone Scardapane has authored 96 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 61 papers in Artificial Intelligence, 27 papers in Computer Vision and Pattern Recognition and 18 papers in Signal Processing. Recurrent topics in Simone Scardapane's work include Machine Learning and ELM (18 papers), Neural Networks and Applications (18 papers) and Domain Adaptation and Few-Shot Learning (14 papers). Simone Scardapane is often cited by papers focused on Machine Learning and ELM (18 papers), Neural Networks and Applications (18 papers) and Domain Adaptation and Few-Shot Learning (14 papers). Simone Scardapane collaborates with scholars based in Italy, United Kingdom and Spain. Simone Scardapane's co-authors include Aurelio Uncini, Dianhui Wang, Amir Hussain, Danilo Comminiello, Indro Spinelli, Massimo Panella, Michele Scarpiniti, Kaizhu Huang, Vinay Chamola and Mufti Mahmud and has published in prestigious journals such as Scientific Reports, Applied Energy and Expert Systems with Applications.

In The Last Decade

Simone Scardapane

84 papers receiving 2.6k citations

Hit Papers

Interpreting Black-Box Mode... 2017 2026 2020 2023 2023 2017 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Simone Scardapane Italy 25 1.6k 558 417 252 231 96 2.7k
Qianli Ma China 26 1.4k 0.9× 757 1.4× 370 0.9× 496 2.0× 482 2.1× 101 3.2k
Afshin Rostamizadeh United States 21 1.5k 0.9× 800 1.4× 179 0.4× 144 0.6× 172 0.7× 34 2.6k
Jian Yu China 28 761 0.5× 917 1.6× 295 0.7× 219 0.9× 166 0.7× 181 3.4k
Naveed Akhtar Australia 22 1.4k 0.9× 1.3k 2.4× 218 0.5× 356 1.4× 398 1.7× 150 3.4k
Yimin Yang China 30 1.0k 0.7× 870 1.6× 458 1.1× 182 0.7× 106 0.5× 149 3.1k
Ameet Talwalkar United States 28 2.0k 1.3× 837 1.5× 218 0.5× 332 1.3× 356 1.5× 52 3.8k
Jing Zhao China 20 848 0.5× 593 1.1× 167 0.4× 164 0.7× 108 0.5× 128 2.5k
Antonia Creswell United Kingdom 6 988 0.6× 1.1k 2.0× 223 0.5× 242 1.0× 85 0.4× 6 3.0k
Marius Kloft Germany 25 1.7k 1.1× 998 1.8× 147 0.4× 325 1.3× 194 0.8× 86 3.0k
Chiranjib Bhattacharyya India 20 1.6k 1.0× 973 1.7× 186 0.4× 285 1.1× 212 0.9× 90 3.4k

Countries citing papers authored by Simone Scardapane

Since Specialization
Citations

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

Fields of papers citing papers by Simone Scardapane

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Simone Scardapane

This figure shows the co-authorship network connecting the top 25 collaborators of Simone Scardapane. A scholar is included among the top collaborators of Simone Scardapane 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 Simone Scardapane. Simone Scardapane 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.
Spinelli, Indro, et al.. (2025). Adaptive token selection for scalable point cloud transformers. Neural Networks. 188. 107477–107477.
2.
Scardapane, Simone, et al.. (2025). NACHOS: Neural Architecture Search for Hardware-Constrained Early-Exit Neural Networks. IEEE Transactions on Neural Networks and Learning Systems. 36(10). 19342–19355.
3.
Lorenzo, Paolo Di, et al.. (2025). Adaptive layer and token selection for efficient fine-tuning of vision transformers. Neurocomputing. 654. 131216–131216. 1 indexed citations
4.
Scardapane, Simone, et al.. (2025). Task Singular Vectors: Reducing Task Interference in Model Merging. IRIS Research product catalog (Sapienza University of Rome). 18695–18705. 1 indexed citations
5.
Gargiulo, Simona, et al.. (2025). Influence based explainability of brain tumors segmentation in magnetic resonance imaging. Progress in Artificial Intelligence. 2 indexed citations
6.
Contu, Riccardo, et al.. (2025). Towards zero-shot learning in 3D change detection: improving generalization with custom augmentations and evaluation. European Journal of Remote Sensing. 58(1).
7.
Scardapane, Simone, et al.. (2024). A Simple and Effective L_2 Norm-Based Strategy for KV Cache Compression. 18476–18499.
8.
Strinati, Emilio Calvanese, Paolo Di Lorenzo, Vincenzo Sciancalepore, et al.. (2024). Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approach. SPIRE - Sciences Po Institutional REpository. 1–6. 28 indexed citations
10.
Nicoletti, Lorenzo, et al.. (2023). Convergent Approaches to AI Explainability for HEP Muonic Particles Pattern Recognition. IRIS Research product catalog (Sapienza University of Rome). 7(1). 2 indexed citations
11.
Scardapane, Simone, et al.. (2023). Continual learning with invertible generative models. Neural Networks. 164. 606–616. 2 indexed citations
12.
Hassija, Vikas, Vinay Chamola, A. Mahapatra, et al.. (2023). Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence. Cognitive Computation. 16(1). 45–74. 672 indexed citations breakdown →
13.
Comminiello, Danilo, et al.. (2022). A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling. IEEE Transactions on Systems Man and Cybernetics Systems. 53(3). 1384–1396. 10 indexed citations
14.
Scardapane, Simone, et al.. (2021). Structured ensembles. An approach to reduce the memory footprint of ensemble methods. IRIS Research product catalog (Sapienza University of Rome). 4 indexed citations
15.
Scardapane, Simone, Steven Van Vaerenbergh, Amir Hussain, & Aurelio Uncini. (2020). Complex-valued neural networks with nonparametric activation functions. IRIS Research product catalog (Sapienza University of Rome). 53 indexed citations
16.
Scardapane, Simone, et al.. (2019). Kafnets: Kernel-based non-parametric activation functions for neural networks. IRIS Research product catalog (Sapienza University of Rome). 48 indexed citations
17.
Vaerenbergh, Steven Van, Simone Scardapane, & Ignacio Santamarı́a. (2018). Recursive multikernel filters exploiting nonlinear temporal structure. UCrea (University of Cantabria). 2 indexed citations
18.
Scardapane, Simone & Paolo Di Lorenzo. (2018). Stochastic training of neural networks via successive convex approximations. IRIS Research product catalog (Sapienza University of Rome). 7 indexed citations
19.
Scardapane, Simone, Michele Scarpiniti, Danilo Comminiello, & Aurelio Uncini. (2016). Diffusion spline adaptive filtering. IRIS Research product catalog (Sapienza University of Rome). 1498–1502. 25 indexed citations
20.
Comminiello, Danilo, Simone Scardapane, Michele Scarpiniti, & Aurelio Uncini. (2013). User-driven quality enhancement for audio signal processing. Journal of the Audio Engineering Society. 608–615. 4 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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