Matthew L. Leavitt
- Computer Vision and Pattern Recognition top 5%
- Cognitive Neuroscience top 5%
- Artificial Intelligence top 10%
- Cellular and Molecular Neuroscience
- Radiology, Nuclear Medicine and Imaging
- Co-authors
- Stéphane d’AscoliAri S. MorcosGiulio BiroliLevent SagunHugo TouvronJulio Martínez-TrujilloDiego Mendoza-HallidayAdam Sachs
- Topics
- Neural dynamics and brain function (6 papers)Visual perception and processing mechanisms (4 papers)Memory and Neural Mechanisms (4 papers)
- Partner nations
- CanadaGermanyUnited States
In The Last Decade
Matthew L. Leavitt
12 papers receiving 761 citations
Hit Papers
Peers
Comparison fields: 5 of 103
- Computer Vision and Pattern Recognition 282
- Cognitive Neuroscience 269
- Artificial Intelligence 201
- Cellular and Molecular Neuroscience 78
- Radiology, Nuclear Medicine and Imaging 60
Countries citing papers authored by Matthew L. Leavitt
This map shows the geographic impact of Matthew L. Leavitt'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 Matthew L. Leavitt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew L. Leavitt more than expected).
Fields of papers citing papers by Matthew L. Leavitt
This network shows the impact of papers produced by Matthew L. Leavitt. 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 Matthew L. Leavitt. The network helps show where Matthew L. Leavitt may publish in the future.
Co-authorship network of co-authors of Matthew L. Leavitt
This figure shows the co-authorship network connecting the top 25 collaborators of Matthew L. Leavitt. A scholar is included among the top collaborators of Matthew L. Leavitt 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 Matthew L. Leavitt. Matthew L. Leavitt is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 1 | |
| 3 | ConViT: improving vision transformers with soft convolutional inductive biases*breakdown → | 478 |
| 4 | 7 | |
| 5 | 128 | |
| 6 | 66 | |
| 7 | 27 | |
| 8 | 17 | |
| 9 | 22 | |
| 10 | 9 | |
| 11 | Recombinant lysosomal acid lipase normalizes liver weight, transaminases and histopathological abnormalities in an in vivo model of cholesteryl ester storage disease | 1 |
| 12 | Increased titer of recombinant AAV vectors by gene transfer with adenovirus coupled to DNA-polylysine complexes. | 22 |
About Matthew L. Leavitt
Matthew L. Leavitt is a scholar working on Cognitive Neuroscience, Pathology and Forensic Medicine and Cellular and Molecular Neuroscience, having authored 12 papers that have together received 782 indexed citations. Recurring topics across this work include Neural dynamics and brain function (6 papers), Visual perception and processing mechanisms (4 papers) and Memory and Neural Mechanisms (4 papers). The work is most often cited by research in Cognitive Neuroscience (269 citations), Computer Vision and Pattern Recognition (282 citations) and Artificial Intelligence (201 citations). Matthew L. Leavitt has collaborated with scholars based in Canada, Germany and United States. Frequent co-authors include Stéphane d’Ascoli, Ari S. Morcos, Giulio Biroli, Levent Sagun, Hugo Touvron, Julio Martínez-Trujillo, Diego Mendoza-Halliday, Adam Sachs, Florian Pieper and Michael Mamounas. Their work appears in journals such as Proceedings of the National Academy of Sciences, Nature Communications and PLoS ONE.
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.