Alejandro Molina
- Artificial Intelligence top 10%
- Computer Networks and Communications top 10%
- Signal Processing top 10%
- Computer Vision and Pattern Recognition top 10%
- Information Systems top 10%
- Co-authors
- Kristian KerstingCarsten BinnigBenjamin HilprechtAndreas SchmidtSriraam NatarajanAntonio VergariNicola Di MauroFloriana Esposito
- Topics
- Bayesian Modeling and Causal Inference (7 papers)Adversarial Robustness in Machine Learning (3 papers)Machine Learning and Data Classification (3 papers)
- Partner nations
- GermanyUnited KingdomItaly
In The Last Decade
Alejandro Molina
20 papers receiving 313 citations
Peers
Comparison fields: 5 of 73
- Artificial Intelligence 160
- Computer Networks and Communications 101
- Signal Processing 94
- Computer Vision and Pattern Recognition 62
- Information Systems 46
Countries citing papers authored by Alejandro Molina
This map shows the geographic impact of Alejandro Molina'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 Alejandro Molina with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alejandro Molina more than expected).
Fields of papers citing papers by Alejandro Molina
This network shows the impact of papers produced by Alejandro Molina. 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 Alejandro Molina. The network helps show where Alejandro Molina may publish in the future.
Co-authorship network of co-authors of Alejandro Molina
This figure shows the co-authorship network connecting the top 25 collaborators of Alejandro Molina. A scholar is included among the top collaborators of Alejandro Molina 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 Alejandro Molina. Alejandro Molina is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 2 | |
| 3 | Residual Sum-Product Networks | 1 |
| 4 | Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks | 6 |
| 5 | 122 | |
| 6 | 6 | |
| 7 | 14 | |
| 8 | 2 | |
| 9 | 35 | |
| 10 | 6 | |
| 11 | 4 | |
| 12 | 13 | |
| 13 | 12 | |
| 14 | 9 | |
| 15 | 21 | |
| 16 | 13 | |
| 17 | 5 | |
| 18 | 1 | |
| 19 | Cyberspace: The "Color Line" of the 21st Century | 1 |
| 20 | 13 |
About Alejandro Molina
Alejandro Molina is a scholar working on Artificial Intelligence, Museology and Communication, having authored 21 papers that have together received 325 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (7 papers), Adversarial Robustness in Machine Learning (3 papers) and Machine Learning and Data Classification (3 papers). The work is most often cited by research in Signal Processing (94 citations), Artificial Intelligence (160 citations) and Computer Networks and Communications (101 citations). Alejandro Molina has collaborated with scholars based in Germany, United Kingdom and Italy. Frequent co-authors include Kristian Kersting, Carsten Binnig, Benjamin Hilprecht, Andreas Schmidt, Sriraam Natarajan, Antonio Vergari, Nicola Di Mauro, Floriana Esposito, Fabian Hadiji and Jethro Akroyd. Their work appears in journals such as The Astronomical Journal, Machine Learning and Proceedings of the VLDB Endowment.
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.