Dan Feldman
- Computational Mathematics top 10%
- Artificial Intelligence top 5%
- Machine Learning and Algorithms 13
- Stochastic Gradient Optimization Techniques 9
- Signal Processing top 5%
- Data Management and Algorithms 10
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- Face and Expression Recognition 8
- Advanced Neural Network Applications 7
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- Sparse and Compressive Sensing Techniques 19
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- Complexity and Algorithms in Graphs 11
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- Robotics and Sensor-Based Localization 10
- Co-authors
- Daniela RusChristian SohlerMorteza MonemizadehCynthia SungMatthew FaulknerAndreas KrauseAmos FiatDaniel J. Brasier
- Journals
- Sensors (3 papers)Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (2 papers)IEEE Transactions on Neural Networks and Learning Systems (2 papers)
- Partner nations
- IsraelUnited StatesGermany
In The Last Decade
Dan Feldman
84 papers receiving 971 citations
Peers
Comparison fields: 5 of 121
- Computational Mathematics 10
- Artificial Intelligence 472
- Signal Processing 150
- Computer Vision and Pattern Recognition 283
- Computer Graphics and Computer-Aided Design 36
Countries citing papers authored by Dan Feldman
This map shows the geographic impact of Dan Feldman'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 Dan Feldman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dan Feldman more than expected).
Fields of papers citing papers by Dan Feldman
This network shows the impact of papers produced by Dan Feldman. 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 Dan Feldman. The network helps show where Dan Feldman may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Dan Feldman, 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 | 2023 | 1 | |
| 2 | 2023 | 1 | |
| 3 | 2022 | 8 | |
| 4 | Sets Clustering | 2020 | 1 |
| 5 | Data-Independent Neural Pruning via Coresets | 2020 | 3 |
| 6 | PyHammer: Python spectral typing suite | 2020 | 1 |
| 7 | Coresets for Near-Convex Functions | 2020 | 2 |
| 8 | Streaming coreset constructions for M-estimators | 2019 | 4 |
| 9 | k-Means Clustering of Lines for Big Data | 2019 | 5 |
| 10 | On Activation Function Coresets for Network Pruning | 2019 | 1 |
| 11 | Coresets for Vector Summarization with Applications to Network Graphs | 2017 | 1 |
| 12 | Training Mixture Models at Scale via Coresets | 2017 | 8 |
| 13 | 2016 | 12 | |
| 14 | 2015 | 8 | |
| 15 | 2015 | 14 | |
| 16 | Coresets for k-Segmentation of Streaming Data | 2014 | 24 |
| 17 | 2012 | 14 | |
| 18 | 2012 | 4 | |
| 19 | 2010 | 27 | |
| 20 | 2008 | 11 |
About Dan Feldman
Dan Feldman is a scholar working on Computational Mathematics, Computer Graphics and Computer-Aided Design and Computer Vision and Pattern Recognition, having authored 87 papers that have together received 1.0k indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (19 papers), Machine Learning and Algorithms (13 papers), Complexity and Algorithms in Graphs (11 papers), Data Management and Algorithms (10 papers), Robotics and Sensor-Based Localization (10 papers), Stochastic Gradient Optimization Techniques (9 papers), Face and Expression Recognition (8 papers) and Advanced Neural Network Applications (7 papers). The work is most often cited by research in Computational Mathematics (10 citations), Artificial Intelligence (472 citations) and Signal Processing (150 citations). Dan Feldman has collaborated with scholars based in Israel, United States and Germany. Frequent co-authors include Daniela Rus, Christian Sohler, Morteza Monemizadeh, Cynthia Sung, Matthew Faulkner, Andreas Krause, Amos Fiat, Daniel J. Brasier, Daniel E. Shulz and Vincent Jacob. Their work appears in journals such as Sensors, Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, IEEE Transactions on Neural Networks and Learning Systems, IEEE Robotics and Automation Letters and IEEE Transactions on Knowledge and Data Engineering.
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.