Luca Franceschi

546 total citations
10 papers, 77 citations indexed

About

Luca Franceschi is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Theory and Mathematics. According to data from OpenAlex, Luca Franceschi has authored 10 papers receiving a total of 77 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 2 papers in Computer Vision and Pattern Recognition and 2 papers in Computational Theory and Mathematics. Recurrent topics in Luca Franceschi's work include Machine Learning and Algorithms (2 papers), Explainable Artificial Intelligence (XAI) (2 papers) and Robotic Locomotion and Control (2 papers). Luca Franceschi is often cited by papers focused on Machine Learning and Algorithms (2 papers), Explainable Artificial Intelligence (XAI) (2 papers) and Robotic Locomotion and Control (2 papers). Luca Franceschi collaborates with scholars based in Italy, United Kingdom and Germany. Luca Franceschi's co-authors include Massimiliano Pontil, Darwin G. Caldwell, Michele Focchi, Claudio Semini, Marco Camurri, Victor Barasuol, Mathias Niepert, Xiao He, Michele Donini and Paolo Frasconi and has published in prestigious journals such as IEEE Robotics and Automation Letters, HAL (Le Centre pour la Communication Scientifique Directe) and Florence Research (University of Florence).

In The Last Decade

Luca Franceschi

10 papers receiving 75 citations

Peers

Luca Franceschi

BE

CVPR

AI

CSE

AE

Kirsty Ellis United Kingdom

Mel Vecerík United Kingdom

Anthony L. Caterini United Kingdom

Ignasi Clavera United States

Norman Di Palo United Kingdom

Muhammad Bilal Shaikh Australia

Arunkumar Byravan United States

Maximilian Karl Germany

Ted Hesselroth United States

Arliones Hoeller Brazil

Kirsty Ellis United Kingdom

Luca Franceschi

18 ×0.4

BE

68 ×2.1

CVPR

28 ×1.2

AI

30 ×1.8

CSE

25 ×1.5

AE

Citations per year, relative to Luca Franceschi Luca Franceschi (= 1×) peers Kirsty Ellis

Countries citing papers authored by Luca Franceschi

Since Specialization

Citations

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

Fields of papers citing papers by Luca Franceschi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Luca Franceschi

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

All Works

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10 of 10 papers shown

1.

Franceschi, Luca, Michele Donini, Valerio Perrone, et al.. (2025). Hyperparameter Optimization in Machine Learning. Florence Research (University of Florence). 18(6). 1054–1201. 1 indexed citations

2.

Minervini, Pasquale, Luca Franceschi, & Mathias Niepert. (2023). Adaptive Perturbation-Based Gradient Estimation for Discrete Latent Variable Models. Proceedings of the AAAI Conference on Artificial Intelligence. 37(8). 9200–9208. 3 indexed citations

3.

Zafar, Muhammad Bilal, et al.. (2023). Hands-on Tutorial: "Explanations in AI: Methods, Stakeholders and Pitfalls". 5783–5785. 1 indexed citations

4.

Salzo, Saverio, et al.. (2020). On the Iteration Complexity of Hypergradient Computations. International Conference on Machine Learning. 1. 3748–3758. 1 indexed citations

5.

Franceschi, Luca, Mathias Niepert, Massimiliano Pontil, & Xiao He. (2019). Learning Discrete Structures for Graph Neural Networks. UCL Discovery (University College London). 1972–1982. 14 indexed citations

6.

Donini, Michele, et al.. (2019). Scheduling the Learning Rate Via Hypergradients: New Insights and a New Algorithm. 1 indexed citations

7.

Barasuol, Victor, Marco Camurri, Luca Franceschi, et al.. (2019). Fast and Continuous Foothold Adaptation for Dynamic Locomotion Through CNNs. IEEE Robotics and Automation Letters. 4(2). 2140–2147. 48 indexed citations

8.

Barasuol, Victor, Marco Camurri, Michele Focchi, et al.. (2018). Fast and Continuous Foothold Adaptation for Dynamic Locomotion through Convolutional Neural Networks.. 1 indexed citations

9.

Franceschi, Luca, Michele Donini, Paolo Frasconi, & Massimiliano Pontil. (2017). On Hyperparameter Optimization in Learning Systems.. International Conference on Learning Representations. 4 indexed citations

10.

Badino, Leonardo, Luca Franceschi, Raman Arora, Michele Donini, & Massimiliano Pontil. (2017). A Speaker Adaptive DNN Training Approach for Speaker-Independent Acoustic Inversion. HAL (Le Centre pour la Communication Scientifique Directe). 984–988. 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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