Pierre Stock
Impact in
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- Artificial Intelligence in Healthcare and Education
Papers in
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- Adversarial Robustness in Machine Learning 2
- Anomaly Detection Techniques and Applications 1
- Natural Language Processing Techniques 1
- Neural Networks and Applications 1
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- Medical Image Segmentation Techniques 1
- Advanced Neural Network Applications 1
- Co-authors
- Barlas Oğuz (1 shared paper)Yangyang Shi (1 shared paper)Vikas Chandra (1 shared paper)Zechun Liu (1 shared paper)Yashar Mehdad (1 shared paper)Ernie Chang (1 shared paper)Raghuraman Krishnamoorthi (1 shared paper)Moustapha Cissé (1 shared paper)
- Journals
- Constructive Approximation (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- France
In The Last Decade
Pierre Stock
4 papers receiving 60 citations
Peers
Comparison fields: 5 of 27
- Health Informatics 5
- Computational Mathematics 1
- Artificial Intelligence 38
- Computer Vision and Pattern Recognition 15
- Management Science and Operations Research 3
Countries citing papers authored by Pierre Stock
This map shows the geographic impact of Pierre Stock'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 Pierre Stock with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pierre Stock more than expected).
Fields of papers citing papers by Pierre Stock
This network shows the impact of papers produced by Pierre Stock. 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 Pierre Stock. The network helps show where Pierre Stock may publish in the future.
Co-authors
The 14 scholars most cited alongside Pierre Stock, 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 | 51 | |
| 2 | ConvNets and ImageNet Beyond Accuracy: Explanations, Bias Detection, Adversarial Examples and Model Criticism. | 2017 | 8 |
| 3 | Training with Quantization Noise for Extreme Fixed-Point Compression | 2020 | 3 |
| 4 | 2022 | 2 |
About Pierre Stock
Pierre Stock is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Radiology, Nuclear Medicine and Imaging and Infectious Diseases, having authored 4 papers that have together received 64 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (2 papers), Anomaly Detection Techniques and Applications (1 paper), Medical Image Segmentation Techniques (1 paper), Natural Language Processing Techniques (1 paper), Model Reduction and Neural Networks (1 paper), Medical Imaging Techniques and Applications (1 paper), Neural Networks and Applications (1 paper) and Advanced Neural Network Applications (1 paper). The work is most often cited by research in Health Informatics (5 citations), Computational Mathematics (1 citation), Artificial Intelligence (38 citations), Computer Vision and Pattern Recognition (15 citations) and Management Science and Operations Research (3 citations). Pierre Stock has collaborated with scholars based in France. Frequent co-authors include Barlas Oğuz, Yangyang Shi, Vikas Chandra, Zechun Liu, Yashar Mehdad, Ernie Chang, Raghuraman Krishnamoorthi, Moustapha Cissé, Rémi Gribonval and Édouard Grave. Their work appears in journals such as Constructive Approximation and arXiv (Cornell University).
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.