Paolo Frasconi
- Artificial Intelligence top 0.1%
- Neural Networks and Applications 22
- Machine Learning and Algorithms 15
- Natural Language Processing Techniques 15
- Topic Modeling 12
- Signal Processing top 0.5%
- Transportation top 0.5%
- Building and Construction top 0.5%
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- Protein Structure and Dynamics 20
- Machine Learning in Bioinformatics 19
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- Cell Image Analysis Techniques 13
- Advanced Fluorescence Microscopy Techniques 10
Paolo Frasconi
123 papers receiving 9.5k citations
Hit Papers
Peers
Comparison fields: 5 of 211
- Artificial Intelligence 4.2k
- Signal Processing 1.1k
- Transportation 538
- Computer Vision and Pattern Recognition 1.6k
- Building and Construction 804
Countries citing papers authored by Paolo Frasconi
This map shows the geographic impact of Paolo Frasconi'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 Paolo Frasconi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Paolo Frasconi more than expected).
Fields of papers citing papers by Paolo Frasconi
This network shows the impact of papers produced by Paolo Frasconi. 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 Paolo Frasconi. The network helps show where Paolo Frasconi may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Paolo Frasconi, 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 | 2024 | 0 | |
| 2 | 2024 | 1 | |
| 3 | On Hyperparameter Optimization in Learning Systems. | 2017 | 4 |
| 4 | Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2016) | 2016 | 15 |
| 5 | Graph invariant kernels | 2015 | 28 |
| 6 | A Statistical Relational Learning Approach to Identifying Evidence Based Medicine Categories | 2012 | 20 |
| 7 | 2011 | 1 | |
| 8 | Spatial relation extraction using relational learning | 2011 | 4 |
| 9 | Predicting the Geometry of Metal Binding Sites from Protein Sequence | 2008 | 10 |
| 10 | kFOIL: learning simple relational kernels | 2006 | 46 |
| 11 | Kernels on prolog ground terms | 2005 | 2 |
| 12 | Modeling the Internet and the Web: Probabilistic Method and Algorithms | 2003 | 75 |
| 13 | Prediction of Protein Topologies Using Generalized IOHMMs and RNNs | 2002 | 0 |
| 14 | Enhancing first-pass attachment prediction | 2002 | 2 |
| 15 | Special issue on connectionist models for learning in structured domains | 2001 | 1 |
| 16 | Learning efficiently with neural networks: a theoretical comparison between structured and flat representations | 2000 | 7 |
| 17 | A topological transformation for hidden recursive modelsarchitecture networks. | 1999 | 2 |
| 18 | On the efficient classification of data structures by neural networks | 1997 | 7 |
| 19 | Diffusion of Credit in Markovian Models | 1994 | 10 |
| 20 | An Input Output HMM Architecture | 1994 | 168 |
About Paolo Frasconi
Paolo Frasconi is a scholar working on Biophysics, Artificial Intelligence and Computational Theory and Mathematics, having authored 130 papers that have together received 10.0k indexed citations. Recurring topics across this work include Neural Networks and Applications (22 papers), Protein Structure and Dynamics (20 papers), Machine Learning in Bioinformatics (19 papers), Machine Learning and Algorithms (15 papers), Natural Language Processing Techniques (15 papers), Cell Image Analysis Techniques (13 papers), Topic Modeling (12 papers) and Advanced Fluorescence Microscopy Techniques (10 papers). The work is most often cited by research in Artificial Intelligence (4.2k citations), Signal Processing (1.1k citations) and Transportation (538 citations). Paolo Frasconi has collaborated with scholars based in Italy, United States and Belgium. Frequent co-authors include Yoshua Bengio, P. Simard, Marco Gori, Marco Lippi, Andrea Passerini, G. Soda, Matteo Bertini, Alessandro Vullo, Pierre Baldi and Alessio Ceroni. Their work appears in journals such as Bioinformatics, Machine Learning, IEEE Transactions on Knowledge and Data Engineering, Scientific Reports and Nucleic Acids Research.
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.