David J. Schwab
- Computational Mathematics top 5%
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- Photoreceptor and optogenetics research 6
- Neuroscience and Neuropharmacology Research 4
- Cognitive Neuroscience top 10%
- Neural dynamics and brain function 9
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- Gene Regulatory Network Analysis 5
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- Domain Adaptation and Few-Shot Learning 5
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- User Authentication and Security Systems 4
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- Scientific Measurement and Uncertainty Evaluation 3
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- Nonlinear Dynamics and Pattern Formation 3
David J. Schwab
43 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 128
- Computational Mathematics 41
- Cellular and Molecular Neuroscience 253
- Cognitive Neuroscience 230
- Molecular Biology 528
- Statistical and Nonlinear Physics 89
Countries citing papers authored by David J. Schwab
This map shows the geographic impact of David J. Schwab'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 David J. Schwab with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David J. Schwab more than expected).
Fields of papers citing papers by David J. Schwab
This network shows the impact of papers produced by David J. Schwab. 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 David J. Schwab. The network helps show where David J. Schwab may publish in the future.
Co-authorship network
The 25 scholars most cited alongside David J. Schwab, 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 | 2022 | 1 | |
| 2 | Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs | 2021 | 2 |
| 3 | Information-bottleneck renormalization group for self-supervised representation learning | 2020 | 2 |
| 4 | Learning Optimal Representations with the Decodable Information Bottleneck | 2020 | 0 |
| 5 | Conjugate unscented transformation based semi-analytic approach for uncertainty characterization of angles-only initial orbit determination algorithms | 2019 | 1 |
| 6 | The renormalization group and information bottleneck: a unified framework | 2019 | 0 |
| 7 | Supervised Learning with Tensor Networks | 2016 | 103 |
| 8 | 2016 | 62 | |
| 9 | 2015 | 52 | |
| 10 | 2014 | 24 | |
| 11 | 2014 | 59 | |
| 12 | 2013 | 55 | |
| 13 | 2013 | 35 | |
| 14 | 2012 | 38 | |
| 15 | 2011 | 48 | |
| 16 | 2011 | 193 | |
| 17 | 2010 | 2 | |
| 18 | 2010 | 28 | |
| 19 | 2010 | 8 | |
| 20 | 2008 | 24 |
About David J. Schwab
David J. Schwab is a scholar working on Computational Mathematics, Cognitive Neuroscience and Statistics, Probability and Uncertainty, having authored 46 papers that have together received 1.1k indexed citations. Recurring topics across this work include Neural dynamics and brain function (9 papers), Photoreceptor and optogenetics research (6 papers), Domain Adaptation and Few-Shot Learning (5 papers), Gene Regulatory Network Analysis (5 papers), User Authentication and Security Systems (4 papers), Neuroscience and Neuropharmacology Research (4 papers), Scientific Measurement and Uncertainty Evaluation (3 papers) and Nonlinear Dynamics and Pattern Formation (3 papers). The work is most often cited by research in Computational Mathematics (41 citations), Cellular and Molecular Neuroscience (253 citations) and Cognitive Neuroscience (230 citations). David J. Schwab has collaborated with scholars based in United States, Canada and Israel. Frequent co-authors include Pankaj Mehta, E. Miles Stoudenmire, Ned S. Wingreen, Joshua D. Rabinowitz, Gautam B. Awatramani, Stuart Trenholm, Amanda J. McLaughlin, Ilya Nemenman, Santhosh Sethuramanujam and Javad Noorbakhsh.
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.