Steven Basart
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
- Topic Modeling
- Anomaly Detection Techniques and Applications
- Natural Language Processing Techniques
Papers in
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- Adversarial Robustness in Machine Learning 3
- Natural Language Processing Techniques 1
- Topic Modeling 1
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- Advanced Vision and Imaging 1
- Image and Signal Denoising Methods 1
- Advanced Neural Network Applications 1
- Co-authors
- Dan Hendrycks (6 shared papers)Dawn Song (4 shared papers)Jacob Steinhardt (4 shared papers)Kevin Zhao (1 shared paper)Samyak Parajuli (1 shared paper)Tyler Zhu (1 shared paper)Norman Mu (1 shared paper)Fengqiu Wang (1 shared paper)
- Journals
- International Conference on Learning Representations (1 paper)arXiv (Cornell University) (1 paper)2021 IEEE/CVF International Conference on Computer Vision (ICCV) (1 paper)
- Partner nations
- United States
In The Last Decade
Steven Basart
5 papers receiving 1.3k citations
Steven Basart's Hit Papers
Peers
Comparison fields: 5 of 95
- Computer Vision and Pattern Recognition 715
- Artificial Intelligence 985
- Health Informatics 28
- Radiology, Nuclear Medicine and Imaging 80
- Media Technology 30
Countries citing papers authored by Steven Basart
This map shows the geographic impact of Steven Basart'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 Steven Basart with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Steven Basart more than expected).
Fields of papers citing papers by Steven Basart
This network shows the impact of papers produced by Steven Basart. 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 Steven Basart. The network helps show where Steven Basart may publish in the future.
Co-authors
The 16 scholars most cited alongside Steven Basart, 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 | The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization Hit paper breakdown → | 2021 | 622 |
| 2 | Natural Adversarial Examples Hit paper breakdown → | 2021 | 472 |
| 3 | Measuring Massive Multitask Language Understanding Hit paper breakdown → | 2021 | 203 |
| 4 | A Benchmark for Anomaly Segmentation. | 2019 | 24 |
| 5 | A Quantitative Measure of Generative Adversarial Network Distributions | 2017 | 2 |
| 6 | 2024 | 0 |
About Steven Basart
Steven Basart is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Hardware and Architecture and Infectious Diseases, having authored 6 papers that have together received 1.3k indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (3 papers), Physical Unclonable Functions (PUFs) and Hardware Security (1 paper), Advanced Malware Detection Techniques (1 paper), Natural Language Processing Techniques (1 paper), Advanced Vision and Imaging (1 paper), Image and Signal Denoising Methods (1 paper), Topic Modeling (1 paper) and Advanced Neural Network Applications (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (715 citations), Artificial Intelligence (985 citations), Health Informatics (28 citations), Radiology, Nuclear Medicine and Imaging (80 citations) and Media Technology (30 citations). Steven Basart has collaborated with scholars based in United States. Frequent co-authors include Dan Hendrycks, Dawn Song, Jacob Steinhardt, Kevin Zhao, Samyak Parajuli, Tyler Zhu, Norman Mu, Fengqiu Wang, Saurav Kadavath and Justin Gilmer. Their work appears in journals such as International Conference on Learning Representations, arXiv (Cornell University) and 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
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.