Maxime Oquab
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- Advanced Image and Video Retrieval Techniques 3
- Advanced Neural Network Applications 3
- Multimodal Machine Learning Applications 2
- Artificial Intelligence top 2%
- Adversarial Robustness in Machine Learning 2
- Domain Adaptation and Few-Shot Learning 2
- Neural Networks and Applications 1
- Media Technology top 2%
- Signal Processing top 5%
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- Functional Brain Connectivity Studies 2
- Neural dynamics and brain function 1
- Co-authors
- Ivan LaptevLéon BottouJosef ŠivicJean-Rémi KingDavid López-PazRomain CarronYair LakretzValérie Chanoine
- Journals
- Journal of Neuroscience (1 paper)NeuroImage (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- FranceUnited StatesIndia
In The Last Decade
Maxime Oquab
8 papers receiving 2.0k citations
Hit Papers
Peers
Comparison fields: 5 of 153
- Computer Vision and Pattern Recognition 1.2k
- Artificial Intelligence 790
- Media Technology 208
- Signal Processing 106
- Radiology, Nuclear Medicine and Imaging 194
Countries citing papers authored by Maxime Oquab
This map shows the geographic impact of Maxime Oquab'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 Maxime Oquab with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maxime Oquab more than expected).
Fields of papers citing papers by Maxime Oquab
This network shows the impact of papers produced by Maxime Oquab. 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 Maxime Oquab. The network helps show where Maxime Oquab may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Maxime Oquab, 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 | 2025 | 1 | |
| 2 | 2023 | 16 | |
| 3 | 2023 | 0 | |
| 4 | 2020 | 10 | |
| 5 | Learning about an exponential amount of conditional distributions | 2019 | 1 |
| 6 | 2019 | 2 | |
| 7 | Revisiting Classifier Two-Sample Tests for GAN Evaluation and Causal Discovery | 2016 | 3 |
| 8 | Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networksbreakdown → | 2014 | 2011 |
| 9 | Weakly supervised object recognition with convolutional neural networks | 2014 | 38 |
About Maxime Oquab
Maxime Oquab is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Cognitive Neuroscience, having authored 9 papers that have together received 2.1k indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (3 papers), Advanced Neural Network Applications (3 papers), Functional Brain Connectivity Studies (2 papers), Multimodal Machine Learning Applications (2 papers), Adversarial Robustness in Machine Learning (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Neural dynamics and brain function (1 paper) and Neural Networks and Applications (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (1.2k citations), Artificial Intelligence (790 citations) and Media Technology (208 citations). Maxime Oquab has collaborated with scholars based in France, United States and India. Frequent co-authors include Ivan Laptev, Léon Bottou, Josef Šivic, Jean-Rémi King, David López-Paz, Romain Carron, Yair Lakretz, Valérie Chanoine, Stanislas Dehaene and Jean‐Michel Badier. Their work appears in journals such as Journal of Neuroscience, NeuroImage, arXiv (Cornell University) and Neural Information Processing Systems.
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.