Evgeny Andriyash

738 total citations
10 papers, 137 citations indexed

About

Evgeny Andriyash is a scholar working on Artificial Intelligence, Nuclear and High Energy Physics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Evgeny Andriyash has authored 10 papers receiving a total of 137 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 4 papers in Nuclear and High Energy Physics and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Evgeny Andriyash's work include Particle physics theoretical and experimental studies (3 papers), Generative Adversarial Networks and Image Synthesis (3 papers) and High-Energy Particle Collisions Research (2 papers). Evgeny Andriyash is often cited by papers focused on Particle physics theoretical and experimental studies (3 papers), Generative Adversarial Networks and Image Synthesis (3 papers) and High-Energy Particle Collisions Research (2 papers). Evgeny Andriyash collaborates with scholars based in United States, Russia and Canada. Evgeny Andriyash's co-authors include Gregory W. Moore, Frederik Denef, Daniel L. Jafferis, M. H. S. Amin, Grigory Ovanesyan, M. I. Vysotsky, Lorenzo Buffoni, Roger G. Melko, Emile Hoskinson and Arash Vahdat and has published in prestigious journals such as Physics Letters B, Journal of High Energy Physics and Physical review. A.

In The Last Decade

Evgeny Andriyash

10 papers receiving 127 citations

Peers

Evgeny Andriyash

AI

NHEP

SNP

GT

AMPO

Selomit Ramírez-Uribe Mexico

Abdelmalek Abdesselam United States

Christopher Wever Germany

Florian Herren Germany

David Greynat France

F. Wißbrock Germany

Gábor Etesi Hungary

J. Jesús Aguilera-Verdugo Mexico

Sebastian Klein Germany

Yannick Ulrich Switzerland

Selomit Ramírez-Uribe Mexico

Evgeny Andriyash

24 ×0.4

AI

58 ×0.9

NHEP

17 ×0.5

SNP

4 ×0.2

GT

23 ×1.1

AMPO

Citations per year, relative to Evgeny Andriyash Evgeny Andriyash (= 1×) peers Selomit Ramírez-Uribe

Countries citing papers authored by Evgeny Andriyash

Since Specialization

Citations

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

Fields of papers citing papers by Evgeny Andriyash

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Evgeny Andriyash

This figure shows the co-authorship network connecting the top 25 collaborators of Evgeny Andriyash. A scholar is included among the top collaborators of Evgeny Andriyash 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 Evgeny Andriyash. Evgeny Andriyash is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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10 of 10 papers shown

1.

Vahdat, Arash, Evgeny Andriyash, & William G. Macready. (2020). Undirected Graphical Models as Approximate Posteriors. International Conference on Machine Learning. 1. 9680–9689. 1 indexed citations

2.

Buffoni, Lorenzo, et al.. (2020). A path towards quantum advantage in training deep generative models with quantum annealers. Machine Learning Science and Technology. 1(4). 45028–45028. 35 indexed citations

3.

Vahdat, Arash, et al.. (2018). DVAE++: Discrete Variational Autoencoders with Overlapping Transformations. International Conference on Machine Learning. 5035–5044. 2 indexed citations

4.

Vahdat, Arash, Evgeny Andriyash, & William G. Macready. (2018). DVAE#: Discrete Variational Autoencoders with Relaxed Boltzmann Priors. arXiv (Cornell University). 31. 1864–1874. 3 indexed citations

5.

Andriyash, Evgeny, et al.. (2016). Quantum Boltzmann Machine. Bulletin of the American Physical Society. 2016. 16 indexed citations

6.

King, Andrew D., Emile Hoskinson, T. Lanting, Evgeny Andriyash, & M. H. S. Amin. (2016). Degeneracy, degree, and heavy tails in quantum annealing. Physical review. A. 93(5). 11 indexed citations

7.

Andriyash, Evgeny, Frederik Denef, Daniel L. Jafferis, & Gregory W. Moore. (2012). Wall-crossing from supersymmetric galaxies. Journal of High Energy Physics. 2012(1). 29 indexed citations

8.

Andriyash, Evgeny, Frederik Denef, Daniel L. Jafferis, & Gregory W. Moore. (2012). Bound state transformation walls. Journal of High Energy Physics. 2012(3). 20 indexed citations

9.

Andriyash, Evgeny, Grigory Ovanesyan, & M. I. Vysotsky. (2006). The value of B K from the experimental data on CP violation in K mesons and up-to-date values of CKM matrix parameters. Physics of Atomic Nuclei. 69(2). 286–292. 9 indexed citations

10.

Andriyash, Evgeny, Grigory Ovanesyan, & M. I. Vysotsky. (2004). Difference of ε˜ and ε in fitting the parameters of CKM matrix. Physics Letters B. 599(3-4). 253–259. 11 indexed citations

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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