Nadav Cohen
- Computational Mathematics top 10%
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- Advanced Neural Network Applications 2
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- Neural Networks and Applications 3
- Stochastic Gradient Optimization Techniques 3
- Domain Adaptation and Few-Shot Learning 2
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- Sparse and Compressive Sensing Techniques 3
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- Model Reduction and Neural Networks 2
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- Functional Brain Connectivity Studies 2
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- Innovative Energy Harvesting Technologies 2
- Journals
- Optics Letters (1 paper)Journal of Sound and Vibration (1 paper)Translational Psychiatry (1 paper)
- Partner nations
- IsraelUnited StatesAustralia
In The Last Decade
Nadav Cohen
17 papers receiving 158 citations
Peers
Comparison fields: 5 of 58
- Computational Mathematics 9
- Molecular Medicine 17
- Computer Vision and Pattern Recognition 52
- Applied Microbiology and Biotechnology 5
- Artificial Intelligence 63
Countries citing papers authored by Nadav Cohen
This map shows the geographic impact of Nadav Cohen'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 Nadav Cohen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nadav Cohen more than expected).
Fields of papers citing papers by Nadav Cohen
This network shows the impact of papers produced by Nadav Cohen. 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 Nadav Cohen. The network helps show where Nadav Cohen may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Nadav Cohen, 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 | 2021 | 7 | |
| 3 | Implicit Regularization in Deep Learning May Not Be Explainable by Norms | 2020 | 1 |
| 4 | 2020 | 14 | |
| 5 | 2018 | 19 | |
| 6 | A convergence analysis of gradient descent for deep linear neural networks | 2018 | 11 |
| 7 | On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization | 2018 | 56 |
| 8 | Deep Learning and Quantum Physics : A Fundamental Bridge | 2017 | 1 |
| 9 | Deep SimNets | 2016 | 9 |
| 10 | 2016 | 8 | |
| 11 | Notes on Hierarchical Splines, DCLNs and i-theory | 2015 | 11 |
| 12 | 2014 | 5 | |
| 13 | 2013 | 13 | |
| 14 | 2012 | 5 | |
| 15 | (Not) Higher, Stronger or Swifter: Representation of Female Olympic Athletes in the Israeli Press | 2011 | 6 |
| 16 | 2004 | 2 | |
| 17 | Iterative approach for optimizing a multistage interconnection network | 2002 | 1 |
About Nadav Cohen
Nadav Cohen is a scholar working on Artificial Intelligence, Molecular Medicine and Statistical and Nonlinear Physics, having authored 17 papers that have together received 170 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (3 papers), Neural Networks and Applications (3 papers), Stochastic Gradient Optimization Techniques (3 papers), Advanced Neural Network Applications (2 papers), Model Reduction and Neural Networks (2 papers), Functional Brain Connectivity Studies (2 papers), Innovative Energy Harvesting Technologies (2 papers) and Domain Adaptation and Few-Shot Learning (2 papers). The work is most often cited by research in Computational Mathematics (9 citations), Molecular Medicine (17 citations) and Computer Vision and Pattern Recognition (52 citations). Nadav Cohen has collaborated with scholars based in Israel, United States and Australia. Frequent co-authors include Sanjeev Arora, Elad Hazan, Amnon Shashua, Izhak Bucher, Or Sharir, Wei Hu, Noah Golowich, Shlomo Weiss, Khetam Hussein and Alexander Korytny. Their work appears in journals such as Optics Letters, Journal of Sound and Vibration and Translational Psychiatry.
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.