Nathan Silberman
Impact in
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- Advanced Neural Network Applications
- Multimodal Machine Learning Applications
- Advanced Image and Video Retrieval Techniques
- Advanced Vision and Imaging
- Generative Adversarial Networks and Image Synthesis
- Artificial Intelligence top 2%
- Domain Adaptation and Few-Shot Learning
Papers in
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- Generative Adversarial Networks and Image Synthesis 4
- Advanced Vision and Imaging 2
- Video Surveillance and Tracking Methods 1
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- Machine Learning and Data Classification 2
- Anomaly Detection Techniques and Applications 2
- Domain Adaptation and Few-Shot Learning 2
- Co-authors
- Konstantinos Bousmalis (1 shared paper)David Dohan (1 shared paper)Dumitru Erhan (1 shared paper)Dilip Krishnan (1 shared paper)Rob Fergus (2 shared papers)Sergio Guadarrama (3 shared papers)George Papandreou (1 shared paper)Jonathan Huang (1 shared paper)
- Journals
- International Journal of Computer Vision (1 paper)International Conference on Learning Representations (1 paper)SMARTech Repository (Georgia Institute of Technology) (1 paper)arXiv (Cornell University) (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- United StatesUnited KingdomSwitzerland
In The Last Decade
Nathan Silberman
12 papers receiving 1.9k citations
Nathan Silberman's Hit Papers
Peers
Comparison fields: 5 of 120
- Computer Vision and Pattern Recognition 1.2k
- Artificial Intelligence 790
- Media Technology 138
- Geology 62
- Computer Graphics and Computer-Aided Design 34
Countries citing papers authored by Nathan Silberman
This map shows the geographic impact of Nathan Silberman'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 Nathan Silberman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nathan Silberman more than expected).
Fields of papers citing papers by Nathan Silberman
This network shows the impact of papers produced by Nathan Silberman. 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 Nathan Silberman. The network helps show where Nathan Silberman may publish in the future.
Co-authors
The 25 scholars most cited alongside Nathan Silberman, 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 | Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks Hit paper breakdown → | 2017 | 942 |
| 2 | Indoor scene segmentation using a structured light sensor Hit paper breakdown → | 2011 | 326 |
| 3 | Im2Calories: Towards an Automated Mobile Vision Food Diary Hit paper breakdown → | 2015 | 320 |
| 4 | Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models | 2009 | 137 |
| 5 | 2019 | 129 | |
| 6 | 2019 | 49 | |
| 7 | Case for automated detection of diabetic retinopathy | 2010 | 41 |
| 8 | TF-Slim: A Lightweight Library for Defining, Training and Evaluating Complex Models in TensorFlow | 2017 | 11 |
| 9 | 2017 | 11 | |
| 10 | On the rise and fall of ISPs | 2009 | 4 |
| 11 | 2024 | 3 | |
| 12 | Discrepancy Ratio: Evaluating Model Performance When Even Experts Disagree on the Truth | 2020 | 3 |
About Nathan Silberman
Nathan Silberman is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Strategy and Management and Ophthalmology, having authored 12 papers that have together received 2.0k indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (4 papers), Machine Learning and Data Classification (2 papers), Advanced Vision and Imaging (2 papers), Anomaly Detection Techniques and Applications (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Advanced Chemical Sensor Technologies (1 paper), COVID-19 diagnosis using AI (1 paper) and Video Surveillance and Tracking Methods (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (1.2k citations), Artificial Intelligence (790 citations), Media Technology (138 citations), Geology (62 citations) and Computer Graphics and Computer-Aided Design (34 citations). Nathan Silberman has collaborated with scholars based in United States, United Kingdom and Switzerland. Frequent co-authors include Konstantinos Bousmalis, David Dohan, Dumitru Erhan, Dilip Krishnan, Rob Fergus, Sergio Guadarrama, George Papandreou, Jonathan Huang, Kevin Murphy and Austin Myers. Their work appears in journals such as International Journal of Computer Vision, International Conference on Learning Representations, SMARTech Repository (Georgia Institute of Technology), 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.