Pascal Vincent
Impact in
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- Face and Expression Recognition
- Generative Adversarial Networks and Image Synthesis
- Advanced Image and Video Retrieval Techniques
- Artificial Intelligence top 0.05%
- Anomaly Detection Techniques and Applications
- Topic Modeling
- Domain Adaptation and Few-Shot Learning
- Neural Networks and Applications
Papers in
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- Generative Adversarial Networks and Image Synthesis 13
- Face and Expression Recognition 7
- Face recognition and analysis 5
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- Neural Networks and Applications 11
- Domain Adaptation and Few-Shot Learning 10
- Machine Learning and Data Classification 7
- Topic Modeling 6
- Co-authors
- Yoshua Bengio (32 shared papers)Pierre-Antoine Manzagol (4 shared papers)Hugo Larochelle (4 shared papers)Isabelle Lajoie (1 shared paper)Aaron C. Courville (7 shared papers)Samy Bengio (3 shared papers)Dumitru Erhan (3 shared papers)Olivier Delalleau (6 shared papers)
- Journals
- Neural Computation (3 papers)Journal of Machine Learning Research (2 papers)Machine Learning (1 paper)Radiology Artificial Intelligence (1 paper)SAE technical papers on CD-ROM/SAE technical paper series (1 paper)
- Partner nations
- CanadaUnited StatesFrance
In The Last Decade
Pascal Vincent
55 papers receiving 15.8k citations
Pascal Vincent's Hit Papers
Peers
Comparison fields: 5 of 199
- Computer Vision and Pattern Recognition 6.9k
- Artificial Intelligence 7.8k
- Signal Processing 2.2k
- Media Technology 1.0k
- Computational Mathematics 42
Countries citing papers authored by Pascal Vincent
This map shows the geographic impact of Pascal Vincent'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 Pascal Vincent with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pascal Vincent more than expected).
Fields of papers citing papers by Pascal Vincent
This network shows the impact of papers produced by Pascal Vincent. 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 Pascal Vincent. The network helps show where Pascal Vincent may publish in the future.
Co-authors
The 25 scholars most cited alongside Pascal Vincent, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 58 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Extracting and composing robust features with denoising autoencoders Hit paper breakdown → | 2008 | 5207 |
| 2 | Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion Hit paper breakdown → | 2010 | 3770 |
| 3 | Why Does Unsupervised Pre-training Help Deep Learning? Hit paper breakdown → | 2010 | 1560 |
| 4 | A Neural Probabilistic Language Model Hit paper breakdown → | 2000 | 770 |
| 5 | Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering Hit paper breakdown → | 2003 | 682 |
| 6 | A Connection Between Score Matching and Denoising Autoencoders Hit paper breakdown → | 2011 | 605 |
| 7 | Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives Hit paper breakdown → | 2012 | 321 |
| 8 | 2015 | 315 | |
| 9 | Combining modality specific deep neural networks for emotion recognition in video Hit paper breakdown → | 2013 | 298 |
| 10 | 2011 | 287 | |
| 11 | 2020 | 285 | |
| 12 | The Difficulty of Training Deep Architectures and the Effect of Unsupervised Pre-Training | 2009 | 258 |
| 13 | 2004 | 255 | |
| 14 | 2002 | 222 | |
| 15 | 2012 | 162 | |
| 16 | K-Local Hyperplane and Convex Distance Nearest Neighbor Algorithms | 2001 | 151 |
| 17 | 2022 | 149 | |
| 18 | The Need for Open Source Software in Machine Learning | 2007 | 135 |
| 19 | Unsupervised and Transfer Learning Challenge: a Deep Learning Approach | 2011 | 112 |
| 20 | The Manifold Tangent Classifier | 2011 | 97 |
About Pascal Vincent
Pascal Vincent is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing, Statistical and Nonlinear Physics and Statistics and Probability, having authored 58 papers that have together received 16.5k indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (13 papers), Neural Networks and Applications (11 papers), Domain Adaptation and Few-Shot Learning (10 papers), Machine Learning and Data Classification (7 papers), Face and Expression Recognition (7 papers), Topic Modeling (6 papers), Music and Audio Processing (5 papers) and Face recognition and analysis (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (6.9k citations), Artificial Intelligence (7.8k citations), Signal Processing (2.2k citations), Media Technology (1.0k citations) and Computational Mathematics (42 citations). Pascal Vincent has collaborated with scholars based in Canada, United States and France. Frequent co-authors include Yoshua Bengio, Pierre-Antoine Manzagol, Hugo Larochelle, Isabelle Lajoie, Aaron C. Courville, Samy Bengio, Dumitru Erhan, Olivier Delalleau, Nicolas Le Roux and Marie Claude Ouimet. Their work appears in journals such as Neural Computation, Journal of Machine Learning Research, Machine Learning, Radiology Artificial Intelligence and SAE technical papers on CD-ROM/SAE technical paper series.
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.