Thomas Manzini
Impact in
- Artificial Intelligence top 5%
- Sentiment Analysis and Opinion Mining
- Topic Modeling
- Advanced Text Analysis Techniques
- Natural Language Processing Techniques
- Text and Document Classification Technologies
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- Emotion and Mood Recognition
Papers in
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- Multimodal Machine Learning Applications 3
- Advanced Neural Network Applications 2
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- Robotics and Sensor-Based Localization 3
- Military Defense Systems Analysis 2
- Co-authors
- Hai Pham (2 shared papers)Barnabás Póczos (2 shared papers)Paul Pu Liang (2 shared papers)Louis–Philippe Morency (1 shared paper)Robin R. Murphy (9 shared papers)Khyathi Raghavi Chandu (1 shared paper)Sumeet Singh (1 shared paper)Alan W. Black (1 shared paper)
- Journals
- Science Robotics (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Thomas Manzini
11 papers receiving 352 citations
Thomas Manzini's Hit Papers
Peers
Comparison fields: 5 of 47
- Artificial Intelligence 282
- Experimental and Cognitive Psychology 115
- Signal Processing 80
- Computer Vision and Pattern Recognition 114
- Computational Mathematics 2
Countries citing papers authored by Thomas Manzini
This map shows the geographic impact of Thomas Manzini'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 Thomas Manzini with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas Manzini more than expected).
Fields of papers citing papers by Thomas Manzini
This network shows the impact of papers produced by Thomas Manzini. 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 Thomas Manzini. The network helps show where Thomas Manzini may publish in the future.
Co-authors
The 21 scholars most cited alongside Thomas Manzini, 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 | Found in Translation: Learning Robust Joint Representations by Cyclic Translations between Modalities Hit paper breakdown → | 2019 | 272 |
| 2 | 2018 | 51 | |
| 3 | 2018 | 11 | |
| 4 | 2017 | 7 | |
| 5 | 2023 | 7 | |
| 6 | 2022 | 4 | |
| 7 | 2023 | 4 | |
| 8 | 2023 | 3 | |
| 9 | 2023 | 3 | |
| 10 | 2024 | 1 | |
| 11 | 2015 | 1 | |
| 12 | 2025 | 0 | |
| 13 | 2023 | 0 | |
| 14 | 2024 | 0 |
About Thomas Manzini
Thomas Manzini is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering, Artificial Intelligence, Electrical and Electronic Engineering and Computer Networks and Communications, having authored 14 papers that have together received 364 indexed citations. Recurring topics across this work include Robotics and Sensor-Based Localization (3 papers), Multimodal Machine Learning Applications (3 papers), Topic Modeling (3 papers), Natural Language Processing Techniques (2 papers), Sentiment Analysis and Opinion Mining (2 papers), Military Defense Systems Analysis (2 papers), Advanced Neural Network Applications (2 papers) and Vehicular Ad Hoc Networks (VANETs) (2 papers). The work is most often cited by research in Artificial Intelligence (282 citations), Experimental and Cognitive Psychology (115 citations), Signal Processing (80 citations), Computer Vision and Pattern Recognition (114 citations) and Computational Mathematics (2 citations). Thomas Manzini has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Hai Pham, Barnabás Póczos, Paul Pu Liang, Louis–Philippe Morency, Robin R. Murphy, Khyathi Raghavi Chandu, Sumeet Singh, Alan W. Black, Guido Zarrella and Caleb Robinson. Their work appears in journals such as Science Robotics and Proceedings of the AAAI Conference on Artificial Intelligence.
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.