Michael Arbel

20 papers receiving 127 citations

Peers

Michael Arbel
Comparison fields: 5 of 27
  • Aerospace Engineering 89
  • Atomic and Molecular Physics, and Optics 100
  • Electrical and Electronic Engineering 114
  • Radiation 9
  • Nuclear and High Energy Physics 7
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Countries citing papers authored by Michael Arbel

Since Specialization
Citations

This map shows the geographic impact of Michael Arbel'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 Michael Arbel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Arbel more than expected).

Fields of papers citing papers by Michael Arbel

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael Arbel. 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 Michael Arbel. The network helps show where Michael Arbel may publish in the future.

Co-authors

The 17 scholars most cited alongside Michael Arbel, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Michael Arbel Line = papers co-authored together Michael Arbel links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.

#Work
1 199538
2 200121
3 199519
4 200015
5
On gradient regularizers for MMD GANs
20187
6 19986
7 19985
8 19974
9 19974
10
A Non-Asymptotic Analysis for Stein Variational Gradient Descent
20203
11 20143
12
Deep Reinforcement Learning with Dynamic Optimism.
20212
13
Maximum Mean Discrepancy Gradient Flow
20192
14 19992
15 20012
16 20022
17 20241
18
Kernelized Wasserstein Natural Gradient
20201
19 19941
20
Efficient and principled score estimation.
20171

About Michael Arbel

Michael Arbel is a scholar working on Electrical and Electronic Engineering, Atomic and Molecular Physics, and Optics, Aerospace Engineering, Artificial Intelligence and Management Science and Operations Research, having authored 21 papers that have together received 139 indexed citations. Recurring topics across this work include Particle accelerators and beam dynamics (12 papers), Particle Accelerators and Free-Electron Lasers (11 papers), Gyrotron and Vacuum Electronics Research (11 papers), Adversarial Robustness in Machine Learning (3 papers), Advanced Neuroimaging Techniques and Applications (1 paper), Electron and X-Ray Spectroscopy Techniques (1 paper), Cell Image Analysis Techniques (1 paper) and Computational Physics and Python Applications (1 paper). The work is most often cited by research in Aerospace Engineering (89 citations), Atomic and Molecular Physics, and Optics (100 citations), Electrical and Electronic Engineering (114 citations), Radiation (9 citations) and Nuclear and High Energy Physics (7 citations). Michael Arbel has collaborated with scholars based in Israel, United Kingdom and United States. Frequent co-authors include A. Gover, Yosef Pinhasi, Amir Abramovich, D. Ben-Haim, A. Kugel, M. Cohen, Arthur Gretton, Mikołaj Bińkowski, Danica J. Sutherland and Aldo Pacchiano. Their work appears in journals such as Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment, Physical Review Letters, Physical Review Special Topics - Accelerators and Beams, Computational and Mathematical Methods in Medicine and UCL Discovery (University College London).

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.

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