Nadav Cohen
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
- Computational Mechanics
- Molecular Medicine
- Topics
- Sparse and Compressive Sensing Techniques (3 papers)Neural Networks and Applications (3 papers)Stochastic Gradient Optimization Techniques (3 papers)
- Partner nations
- IsraelUnited StatesAustralia
In The Last Decade
Nadav Cohen
17 papers receiving 158 citations
Peers
Comparison fields: 5 of 58
- Artificial Intelligence 63
- Computer Vision and Pattern Recognition 52
- Electrical and Electronic Engineering 24
- Computational Mechanics 19
- Molecular Medicine 17
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 of co-authors of Nadav Cohen
This figure shows the co-authorship network connecting the top 25 collaborators of Nadav Cohen. A scholar is included among the top collaborators of Nadav Cohen based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Nadav Cohen. Nadav Cohen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 7 | |
| 3 | Implicit Regularization in Deep Learning May Not Be Explainable by Norms | 1 |
| 4 | 14 | |
| 5 | 19 | |
| 6 | A convergence analysis of gradient descent for deep linear neural networks | 11 |
| 7 | On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization | 56 |
| 8 | Deep Learning and Quantum Physics : A Fundamental Bridge | 1 |
| 9 | Deep SimNets | 9 |
| 10 | 8 | |
| 11 | Notes on Hierarchical Splines, DCLNs and i-theory | 11 |
| 12 | 5 | |
| 13 | 13 | |
| 14 | 5 | |
| 15 | (Not) Higher, Stronger or Swifter: Representation of Female Olympic Athletes in the Israeli Press | 6 |
| 16 | 2 | |
| 17 | Iterative approach for optimizing a multistage interconnection network | 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) and Stochastic Gradient Optimization Techniques (3 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.