François Rivest
- Cognitive Neuroscience top 10%
- Neural dynamics and brain function 10
- Neuroscience and Music Perception 6
- Artificial Intelligence top 10%
- Neural Networks and Applications 5
- Reinforcement Learning in Robotics 2
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- Child and Animal Learning Development 2
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- Context-Aware Activity Recognition Systems 3
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- Single-cell and spatial transcriptomics 2
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- Blind Source Separation Techniques 2
- Co-authors
- Thomas R. ShultzElliot A. LudvigFélix NaefYoji TabataAndrea ManfrinMatthias P. LütolfÉric R. PaquetJean‐Philippe Thivierge
- Journals
- Journal of Mathematical Psychology (2 papers)Nature Methods (1 paper)Scientific Reports (1 paper)
- Partner nations
- CanadaUnited StatesSwitzerland
In The Last Decade
François Rivest
32 papers receiving 446 citations
Peers
Comparison fields: 5 of 97
- Cognitive Neuroscience 145
- Artificial Intelligence 97
- Developmental and Educational Psychology 38
- Music 9
- Statistics and Probability 20
Countries citing papers authored by François Rivest
This map shows the geographic impact of François Rivest'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 François Rivest with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites François Rivest more than expected).
Fields of papers citing papers by François Rivest
This network shows the impact of papers produced by François Rivest. 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 François Rivest. The network helps show where François Rivest may publish in the future.
Co-authorship network
The 25 scholars most cited alongside François Rivest, 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 | 1 | |
| 2 | 2024 | 4 | |
| 3 | 2024 | 2 | |
| 4 | 2023 | 1 | |
| 5 | 2021 | 2 | |
| 6 | 2021 | 11 | |
| 7 | 2021 | 9 | |
| 8 | 2019 | 9 | |
| 9 | 2019 | 123 | |
| 10 | 2018 | 9 | |
| 11 | 2017 | 9 | |
| 12 | 2016 | 12 | |
| 13 | 2013 | 11 | |
| 14 | 2013 | 28 | |
| 15 | 2009 | 17 | |
| 16 | 2007 | 20 | |
| 17 | Compositionality in a Knowledge-Based Constructive Learner. | 2004 | 2 |
| 18 | Combining TD-learning with cascade-correlation networks | 2003 | 18 |
| 19 | 2001 | 37 | |
| 20 | Using Knowledge to Speed Learning: A Comparison of Knowledge-based Cascade-correlation and Multi-task Learning | 2000 | 8 |
About François Rivest
François Rivest is a scholar working on Cognitive Neuroscience, Signal Processing and Developmental Biology, having authored 32 papers that have together received 467 indexed citations. Recurring topics across this work include Neural dynamics and brain function (10 papers), Neuroscience and Music Perception (6 papers), Neural Networks and Applications (5 papers), Context-Aware Activity Recognition Systems (3 papers), Single-cell and spatial transcriptomics (2 papers), Blind Source Separation Techniques (2 papers), Reinforcement Learning in Robotics (2 papers) and Child and Animal Learning Development (2 papers). The work is most often cited by research in Cognitive Neuroscience (145 citations), Artificial Intelligence (97 citations) and Developmental and Educational Psychology (38 citations). François Rivest has collaborated with scholars based in Canada, United States and Switzerland. Frequent co-authors include Thomas R. Shultz, Elliot A. Ludvig, Félix Naef, Yoji Tabata, Andrea Manfrin, Matthias P. Lütolf, Éric R. Paquet, Jean‐Philippe Thivierge, Yoshua Bengio and John Kalaska. Their work appears in journals such as Journal of Mathematical Psychology, Nature Methods, Scientific Reports, IEEE Transactions on Artificial Intelligence and Journal of Computational Neuroscience.
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.