Carlo Baldassi
Impact in
- Artificial Intelligence top 2%
- Neural Networks and Applications
- Stochastic Gradient Optimization Techniques
- Domain Adaptation and Few-Shot Learning
- Cognitive Neuroscience top 10%
- Neural dynamics and brain function
Papers in
-
- Neural Networks and Applications 15
- Stochastic Gradient Optimization Techniques 8
- Machine Learning and ELM 5
- Algorithms and Data Compression 3
- Co-authors
- Riccardo ZecchinaCarlo LucibelloLuca SagliettiAndrea PagnaniAlessandro IngrossoAlfredo BraunsteinMarco ZamparoMartin Weigt
- Journals
- Proceedings of the National Academy of Sciences (5 papers)Physical Review Letters (3 papers)Physical review. E (3 papers)PLoS Computational Biology (2 papers)Journal of Statistical Physics (1 paper)
- Partner nations
- ItalyFranceUnited States
In The Last Decade
Carlo Baldassi
35 papers receiving 942 citations
Peers
Comparison fields: 5 of 112
- Artificial Intelligence 480
- Cognitive Neuroscience 193
- Acoustics and Ultrasonics 8
- Statistical and Nonlinear Physics 104
- Computer Vision and Pattern Recognition 156
Countries citing papers authored by Carlo Baldassi
This map shows the geographic impact of Carlo Baldassi'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 Carlo Baldassi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Carlo Baldassi more than expected).
Fields of papers citing papers by Carlo Baldassi
This network shows the impact of papers produced by Carlo Baldassi. 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 Carlo Baldassi. The network helps show where Carlo Baldassi may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Carlo Baldassi, 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 | 2023 | 6 | |
| 2 | 2023 | 5 | |
| 3 | 2022 | 5 | |
| 4 | 2022 | 16 | |
| 5 | 2021 | 15 | |
| 6 | 2020 | 6 | |
| 7 | On the geometry of solutions and on the capacity of multi-layer neural networks with ReLU activations. | 2019 | 1 |
| 8 | Recombinator-k-means: Enhancing k-means++ by seeding from pools of previous runs. | 2019 | 2 |
| 9 | 2019 | 35 | |
| 10 | 2019 | 1 | |
| 11 | 2018 | 12 | |
| 12 | 2018 | 3 | |
| 13 | 2017 | 35 | |
| 14 | 2016 | 75 | |
| 15 | 2016 | 13 | |
| 16 | 2016 | 76 | |
| 17 | 2015 | 12 | |
| 18 | 2015 | 61 | |
| 19 | 2014 | 95 | |
| 20 | 2013 | 73 |
About Carlo Baldassi
Carlo Baldassi is a scholar working on Artificial Intelligence, General Decision Sciences, Cognitive Neuroscience, Statistical and Nonlinear Physics and Statistics and Probability, having authored 35 papers that have together received 971 indexed citations. Recurring topics across this work include Neural Networks and Applications (15 papers), Neural dynamics and brain function (9 papers), Stochastic Gradient Optimization Techniques (8 papers), Advanced Memory and Neural Computing (7 papers), Machine Learning and ELM (5 papers), Advanced Neural Network Applications (4 papers), Bioinformatics and Genomic Networks (3 papers) and Algorithms and Data Compression (3 papers). The work is most often cited by research in Artificial Intelligence (480 citations), Cognitive Neuroscience (193 citations), Acoustics and Ultrasonics (8 citations), Statistical and Nonlinear Physics (104 citations) and Computer Vision and Pattern Recognition (156 citations). Carlo Baldassi has collaborated with scholars based in Italy, France and United States. Frequent co-authors include Riccardo Zecchina, Carlo Lucibello, Luca Saglietti, Andrea Pagnani, Alessandro Ingrosso, Alfredo Braunstein, Marco Zamparo, Martin Weigt, Jennifer Chayes and Christian Borgs. Their work appears in journals such as Proceedings of the National Academy of Sciences, Physical Review Letters, Physical review. E, PLoS Computational Biology and Journal of Statistical Physics.
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.