Ludwig Schmidt
Impact in
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- Multimodal Machine Learning Applications
- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
- Artificial Intelligence top 2%
- Domain Adaptation and Few-Shot Learning
- Adversarial Robustness in Machine Learning
- Anomaly Detection Techniques and Applications
- Topic Modeling
Papers in
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- Machine Learning and Algorithms 7
- Domain Adaptation and Few-Shot Learning 6
- Adversarial Robustness in Machine Learning 5
- Anomaly Detection Techniques and Applications 4
- Stochastic Gradient Optimization Techniques 3
- Co-authors
- Piotr Indyk (13 shared papers)Chinmay Hegde (9 shared papers)Rebecca Roelofs (4 shared papers)Aleksander Mądry (4 shared papers)Dimitris Tsipras (2 shared papers)Mark Iwen (1 shared paper)Anna C. Gilbert (1 shared paper)Vaishaal Shankar (6 shared papers)
- Journals
- IEEE Signal Processing Magazine (1 paper)IEEE Transactions on Information Theory (1 paper)Conference on Learning Theory (1 paper)Bulletin of the European Association for Theoretical Computer Science (1 paper)arXiv (Cornell University) (5 papers)
- Partner nations
- United StatesNetherlandsGermany
In The Last Decade
Ludwig Schmidt
33 papers receiving 921 citations
Ludwig Schmidt's Hit Papers
Peers
Comparison fields: 5 of 114
- Computer Vision and Pattern Recognition 403
- Artificial Intelligence 552
- Signal Processing 125
- Health Informatics 12
- Computational Mathematics 4
Countries citing papers authored by Ludwig Schmidt
This map shows the geographic impact of Ludwig Schmidt'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 Ludwig Schmidt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ludwig Schmidt more than expected).
Fields of papers citing papers by Ludwig Schmidt
This network shows the impact of papers produced by Ludwig Schmidt. 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 Ludwig Schmidt. The network helps show where Ludwig Schmidt may publish in the future.
Co-authors
The 25 scholars most cited alongside Ludwig Schmidt, 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 38 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Robust fine-tuning of zero-shot models Hit paper breakdown → | 2022 | 208 |
| 2 | 2015 | 113 | |
| 3 | 2014 | 109 | |
| 4 | A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations | 2017 | 99 |
| 5 | Adversarially Robust Generalization Requires More Data | 2018 | 56 |
| 6 | A Meta-Analysis of Overfitting in Machine Learning | 2019 | 53 |
| 7 | Do ImageNet Classifiers Generalize to ImageNet | 2019 | 38 |
| 8 | A Nearly-Linear Time Framework for Graph-Structured Sparsity | 2015 | 33 |
| 9 | 2015 | 30 | |
| 10 | 2014 | 24 | |
| 11 | Evaluating Machine Accuracy on ImageNet | 2020 | 21 |
| 12 | 2014 | 20 | |
| 13 | 2021 | 19 | |
| 14 | 2015 | 18 | |
| 15 | Are We Learning Yet? A Meta Review of Evaluation Failures Across Machine Learning | 2021 | 17 |
| 16 | 2021 | 13 | |
| 17 | 2013 | 13 | |
| 18 | Measuring Robustness to Natural Distribution Shifts in Image Classification | 2020 | 11 |
| 19 | Fast Algorithms for Structured Sparsity | 2015 | 10 |
| 20 | Differentially private learning of structured discrete distributions | 2015 | 9 |
About Ludwig Schmidt
Ludwig Schmidt is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Mechanics, Signal Processing and Radiology, Nuclear Medicine and Imaging, having authored 38 papers that have together received 974 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (8 papers), Machine Learning and Algorithms (7 papers), Domain Adaptation and Few-Shot Learning (6 papers), Adversarial Robustness in Machine Learning (5 papers), Anomaly Detection Techniques and Applications (4 papers), COVID-19 diagnosis using AI (3 papers), Stochastic Gradient Optimization Techniques (3 papers) and Seismic Imaging and Inversion Techniques (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (403 citations), Artificial Intelligence (552 citations), Signal Processing (125 citations), Health Informatics (12 citations) and Computational Mathematics (4 citations). Ludwig Schmidt has collaborated with scholars based in United States, Netherlands and Germany. Frequent co-authors include Piotr Indyk, Chinmay Hegde, Rebecca Roelofs, Aleksander Mądry, Dimitris Tsipras, Mark Iwen, Anna C. Gilbert, Vaishaal Shankar, Gabriel Ilharco and Benjamin Recht. Their work appears in journals such as IEEE Signal Processing Magazine, IEEE Transactions on Information Theory, Conference on Learning Theory, Bulletin of the European Association for Theoretical Computer Science 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.