Mark Palatucci
Impact in
- Artificial Intelligence top 5%
- Domain Adaptation and Few-Shot Learning
- Topic Modeling
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- Multimodal Machine Learning Applications
Papers in
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- Machine Learning and Algorithms 3
- Domain Adaptation and Few-Shot Learning 3
- Adversarial Robustness in Machine Learning 2
- Reinforcement Learning in Robotics 2
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- Autonomous Vehicle Technology and Safety 2
- Co-authors
- Tom M. Mitchell (3 shared papers)Dean Pomerleau (2 shared papers)Geoffrey E. Hinton (1 shared paper)Han Liu (1 shared paper)Jian Zhang (1 shared paper)Leila Wehbe (1 shared paper)Riitta Salmelin (1 shared paper)Gustavo Sudre (1 shared paper)
- Journals
- NeuroImage (1 paper)2022 International Conference on Robotics and Automation (ICRA) (1 paper)Figshare (1 paper)2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (1 paper)
- Partner nations
- United StatesGermanyFinland
In The Last Decade
Mark Palatucci
7 papers receiving 563 citations
Mark Palatucci's Hit Papers
Peers
Comparison fields: 5 of 83
- Artificial Intelligence 352
- Computer Vision and Pattern Recognition 218
- Computational Mathematics 5
- Cognitive Neuroscience 102
- Automotive Engineering 46
Countries citing papers authored by Mark Palatucci
This map shows the geographic impact of Mark Palatucci'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 Mark Palatucci with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Palatucci more than expected).
Fields of papers citing papers by Mark Palatucci
This network shows the impact of papers produced by Mark Palatucci. 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 Mark Palatucci. The network helps show where Mark Palatucci may publish in the future.
Co-authors
The 22 scholars most cited alongside Mark Palatucci, 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 | Zero-Shot Learning with Semantic Output Codes Hit paper breakdown → | 2018 | 294 |
| 2 | 2009 | 130 | |
| 3 | 2012 | 89 | |
| 4 | 2022 | 29 | |
| 5 | 2022 | 28 | |
| 6 | Thought recognition: predicting and decoding brain activity using the zero-shot learning model | 2011 | 4 |
| 7 | 2008 | 4 |
About Mark Palatucci
Mark Palatucci is a scholar working on Artificial Intelligence, Automotive Engineering, Statistics and Probability, Molecular Biology and Cognitive Neuroscience, having authored 7 papers that have together received 578 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (3 papers), Domain Adaptation and Few-Shot Learning (3 papers), Autonomous Vehicle Technology and Safety (2 papers), Adversarial Robustness in Machine Learning (2 papers), Reinforcement Learning in Robotics (2 papers), Statistical Methods and Inference (2 papers), Statistical Methods in Clinical Trials (1 paper) and EEG and Brain-Computer Interfaces (1 paper). The work is most often cited by research in Artificial Intelligence (352 citations), Computer Vision and Pattern Recognition (218 citations), Computational Mathematics (5 citations), Cognitive Neuroscience (102 citations) and Automotive Engineering (46 citations). Mark Palatucci has collaborated with scholars based in United States, Germany and Finland. Frequent co-authors include Tom M. Mitchell, Dean Pomerleau, Geoffrey E. Hinton, Han Liu, Jian Zhang, Leila Wehbe, Riitta Salmelin, Gustavo Sudre, Alona Fyshe and Dragomir Anguelov. Their work appears in journals such as NeuroImage, 2022 International Conference on Robotics and Automation (ICRA), Figshare and 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
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.