Federico Bianchi
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Radiology, Nuclear Medicine and Imaging top 10%
- Atomic and Molecular Physics, and Optics
- Health Informatics top 2%
- Co-authors
- James ZouMert YüksekgönülZhi HuangThomas J. MontineDebora NozzaDirk HovyS. S. StraupeS. P. Kulik
- Topics
- Topic Modeling (15 papers)Natural Language Processing Techniques (9 papers)Explainable Artificial Intelligence (XAI) (3 papers)
- Partner nations
- ItalyUnited StatesRussia
In The Last Decade
Federico Bianchi
27 papers receiving 685 citations
Hit Papers
Peers
Comparison fields: 5 of 107
- Artificial Intelligence 435
- Computer Vision and Pattern Recognition 122
- Radiology, Nuclear Medicine and Imaging 113
- Atomic and Molecular Physics, and Optics 69
- Health Informatics 65
Countries citing papers authored by Federico Bianchi
This map shows the geographic impact of Federico Bianchi'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 Federico Bianchi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Federico Bianchi more than expected).
Fields of papers citing papers by Federico Bianchi
This network shows the impact of papers produced by Federico Bianchi. 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 Federico Bianchi. The network helps show where Federico Bianchi may publish in the future.
Co-authorship network of co-authors of Federico Bianchi
This figure shows the co-authorship network connecting the top 25 collaborators of Federico Bianchi. A scholar is included among the top collaborators of Federico Bianchi 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 Federico Bianchi. Federico Bianchi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | Optimizing generative AI by backpropagating language model feedbackbreakdown → | 18 |
| 3 | 0 | |
| 4 | 10 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scalebreakdown → | 118 |
| 8 | A visual–language foundation model for pathology image analysis using medical Twitterbreakdown → | 263 |
| 9 | 1 | |
| 10 | 1 | |
| 11 | 2 | |
| 12 | 7 | |
| 13 | 4 | |
| 14 | 10 | |
| 15 | Neuro-Symbolic Deductive Reasoning for Cross-Knowledge Graph Entailment. | 2 |
| 16 | 4 | |
| 17 | 15 | |
| 18 | On the Capabilities of Logic Tensor Networks for Deductive Reasoning. | 3 |
| 19 | Type Vector Representations from Text: An Empirical Analysis. | 1 |
| 20 | Joint Learning of Entity and Type Embeddings for Analogical Reasoning with Entities. | 2 |
About Federico Bianchi
Federico Bianchi is a scholar working on Artificial Intelligence, Communication and Software, having authored 28 papers that have together received 702 indexed citations. Recurring topics across this work include Topic Modeling (15 papers), Natural Language Processing Techniques (9 papers) and Explainable Artificial Intelligence (XAI) (3 papers). The work is most often cited by research in Health Informatics (65 citations), Artificial Intelligence (435 citations) and Computer Vision and Pattern Recognition (122 citations). Federico Bianchi has collaborated with scholars based in Italy, United States and Russia. Frequent co-authors include James Zou, Mert Yüksekgönül, Zhi Huang, Thomas J. Montine, Debora Nozza, Dirk Hovy, S. S. Straupe, S. P. Kulik, Dmitry Yudin and Jacob Biamonte. Their work appears in journals such as Nature, Nature Medicine and Scientific Reports.
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.