Dmitry Yarotsky

1.9k total citations · 1 hit paper
31 papers, 955 citations indexed

About

Dmitry Yarotsky is a scholar working on Electrical and Electronic Engineering, Atomic and Molecular Physics, and Optics and Statistical and Nonlinear Physics. According to data from OpenAlex, Dmitry Yarotsky has authored 31 papers receiving a total of 955 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Electrical and Electronic Engineering, 7 papers in Atomic and Molecular Physics, and Optics and 5 papers in Statistical and Nonlinear Physics. Recurrent topics in Dmitry Yarotsky's work include Advanced Wireless Communication Techniques (11 papers), Advanced MIMO Systems Optimization (9 papers) and Millimeter-Wave Propagation and Modeling (7 papers). Dmitry Yarotsky is often cited by papers focused on Advanced Wireless Communication Techniques (11 papers), Advanced MIMO Systems Optimization (9 papers) and Millimeter-Wave Propagation and Modeling (7 papers). Dmitry Yarotsky collaborates with scholars based in Russia, Germany and Belarus. Dmitry Yarotsky's co-authors include С. В. Гапоненко, Dmitry N. Chigrin, Andrei V. Lavrinenko, I. I. Kalosha, А. М. Капитонов, Eugene P. Petrov, J. F. McGilp, Alexander Eychmueller, Andrey L. Rogach and V. N. Bogomolov and has published in prestigious journals such as PLoS ONE, IEEE Access and IEEE Transactions on Wireless Communications.

In The Last Decade

Dmitry Yarotsky

30 papers receiving 915 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Error bounds for approximations with deep ReLU networks

2017 549 citations Dmitry Yarotsky Neural Networks profile →

Peers

Dmitry Yarotsky

AI

EEE

AMPO

SNP

CVPR

Pierre Barthelemy Italy

Yongmei Huang China

Brando Miranda United States

Lixing Han United States

Jin Guo China

Griff L. Bilbro United States

Junsheng Zhao China

Lie‐Quan Lee United States

J. Barhen United States

Pierre Barthelemy Italy

Dmitry Yarotsky

208 ×0.7

AI

213 ×0.8

EEE

491 ×2.2

AMPO

174 ×0.9

SNP

27 ×0.2

CVPR

Citations per year, relative to Dmitry Yarotsky Dmitry Yarotsky (= 1×) peers Pierre Barthelemy

Countries citing papers authored by Dmitry Yarotsky

Since Specialization

Citations

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

Fields of papers citing papers by Dmitry Yarotsky

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dmitry Yarotsky

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Yarotsky, Dmitry. (2024). Learnability of high-dimensional targets by two-parameter models and gradient flow. 79144–79167.

2.

Yarotsky, Dmitry, et al.. (2023). Beamspace Selection in Multi-User Massive MIMO. IEEE Access. 11. 18761–18771. 1 indexed citations

3.

Nabieva, Elena, et al.. (2022). Human genotype-to-phenotype predictions: Boosting accuracy with nonlinear models. PLoS ONE. 17(8). e0273293–e0273293. 8 indexed citations

4.

Yarotsky, Dmitry, et al.. (2021). Spatial Denoising for Sparse Channel Estimation in Coherent Massive MIMO. 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall). 1–5. 4 indexed citations

5.

Yarotsky, Dmitry, et al.. (2021). Adaptive Channel Interpolation in High-Speed Massive MIMO. 1–5. 6 indexed citations

6.

Yarotsky, Dmitry, et al.. (2021). Efficient Performance Bound for Channel Estimation in Massive MIMO Receiver. IEEE Transactions on Wireless Communications. 20(11). 7001–7010. 3 indexed citations

7.

Yarotsky, Dmitry, et al.. (2020). The phase diagram of approximation rates for deep neural networks. Neural Information Processing Systems. 33. 13005–13015. 9 indexed citations

8.

Nabieva, Elena, Sofya K. Garushyants, Ilya Kanivets, et al.. (2020). Accurate fetal variant calling in the presence of maternal cell contamination. European Journal of Human Genetics. 28(11). 1615–1623. 3 indexed citations

9.

Yarotsky, Dmitry, et al.. (2020). Machine Learning-Assisted PAPR Reduction in Massive MIMO. IEEE Wireless Communications Letters. 10(3). 537–541. 19 indexed citations

10.

Yarotsky, Dmitry, et al.. (2020). Lower Performance Bound for Beamspace Channel Estimation in Massive MIMO. IEEE Wireless Communications Letters. 10(2). 311–314. 11 indexed citations

11.

Yarotsky, Dmitry, et al.. (2019). Iterative Nonlinear Detection and Decoding in Multi-User Massive MIMO. 573–578. 14 indexed citations

12.

Yarotsky, Dmitry. (2018). Optimal approximation of continuous functions by very deep ReLU networks. Conference on Learning Theory. 639–649. 8 indexed citations

13.

Yarotsky, Dmitry, et al.. (2018). Unused Beam Reservation for PAPR Reduction in Massive MIMO System. 1–5. 15 indexed citations

14.

Yarotsky, Dmitry. (2017). Error bounds for approximations with deep ReLU networks. Neural Networks. 94. 103–114. 549 indexed citations breakdown →

15.

Andreev, Konstantin, et al.. (2015). Dynamics of multi-tethered pyramidal satellite formation. Acta Astronautica. 117. 222–230. 14 indexed citations

16.

Yarotsky, Dmitry. (2012). Univariate interpolation by exponential functions and Gaussian RBFs for generic sets of nodes. Journal of Approximation Theory. 166. 163–175. 2 indexed citations

17.

Yarotsky, Dmitry. (2012). Examples of inconsistency in optimization by expected improvement. Journal of Global Optimization. 56(4). 1773–1790. 6 indexed citations

18.

Yarotsky, Dmitry. (2007). Random Walk Analysis of the Commensurate-Incommensurate Transition in the Isotropic Spin-1 Chain. Journal of Statistical Physics. 130(5). 957–981. 2 indexed citations

19.

Chigrin, Dmitry N., et al.. (1999). A dielectric Bragg mirror: Can it be an omnidirectional reflector?. 10(12). 33. 1 indexed citations

20.

Гапоненко, С. В., А. М. Капитонов, Dmitry Yarotsky, et al.. (1999). Synthesis and properties of three-dimensional lattices exhibiting photonic pseudogap in the visible range. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 3734. 358–358. 1 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.

Explore authors with similar magnitude of impact