H. N. Mhaskar

5.9k total citations · 1 hit paper
137 papers, 3.4k citations indexed

About

H. N. Mhaskar is a scholar working on Applied Mathematics, Computational Mechanics and Numerical Analysis. According to data from OpenAlex, H. N. Mhaskar has authored 137 papers receiving a total of 3.4k indexed citations (citations by other indexed papers that have themselves been cited), including 62 papers in Applied Mathematics, 36 papers in Computational Mechanics and 35 papers in Numerical Analysis. Recurrent topics in H. N. Mhaskar's work include Mathematical functions and polynomials (41 papers), Mathematical Approximation and Integration (25 papers) and Neural Networks and Applications (24 papers). H. N. Mhaskar is often cited by papers focused on Mathematical functions and polynomials (41 papers), Mathematical Approximation and Integration (25 papers) and Neural Networks and Applications (24 papers). H. N. Mhaskar collaborates with scholars based in United States, Germany and Hong Kong. H. N. Mhaskar's co-authors include Tomaso Poggio, Charles A. Micchelli, Edward B. Saff, Charles K. Chui, Qianli Liao, Jürgen Prestin, E. B. Saff, Brando Miranda, Lorenzo Rosasco and F. J. Narcowich and has published in prestigious journals such as Journal of Computational Physics, Mathematics of Computation and Neural Computation.

In The Last Decade

H. N. Mhaskar

130 papers receiving 3.1k 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.

Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review

2017 304 citations Tomaso Poggio, H. N. Mhaskar et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
H. N. Mhaskar United States	30	1.2k	1.1k	678	611	599	137	3.4k
Allan Pinkus Israel	23	767 0.6×	934 0.8×	353 0.5×	993 1.6×	466 0.8×	95	3.6k
P.-A. Absil Belgium	19	687 0.6×	255 0.2×	981 1.4×	677 1.1×	413 0.7×	38	4.0k
Douglas P. Hardin United States	27	465 0.4×	607 0.5×	1.3k 1.9×	261 0.4×	376 0.6×	99	3.3k
Albert Cohen France	28	628 0.5×	904 0.8×	2.7k 3.9×	163 0.3×	211 0.4×	119	5.2k
Ding‐Xuan Zhou Hong Kong	39	2.2k 1.9×	923 0.8×	1.6k 2.4×	346 0.6×	328 0.5×	187	5.5k
Albert Cohen France	30	240 0.2×	628 0.6×	968 1.4×	422 0.7×	354 0.6×	88	3.5k
Tryphon T. Georgiou United States	42	474 0.4×	438 0.4×	301 0.4×	492 0.8×	1.1k 1.9×	225	5.3k
Fuzhen Zhang United States	26	310 0.3×	1.1k 1.0×	277 0.4×	274 0.4×	435 0.7×	104	4.4k
Adhemar Bultheel Belgium	25	219 0.2×	1.4k 1.2×	525 0.8×	581 1.0×	488 0.8×	278	3.0k
Vladimir Temlyakov United States	30	409 0.3×	1.3k 1.2×	1.2k 1.8×	1.2k 1.9×	140 0.2×	141	4.8k

Countries citing papers authored by H. N. Mhaskar

Since Specialization

Citations

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

Fields of papers citing papers by H. N. Mhaskar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by H. N. Mhaskar. 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 H. N. Mhaskar. The network helps show where H. N. Mhaskar may publish in the future.

Co-authorship network of co-authors of H. N. Mhaskar

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

All Works

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20 of 20 papers shown

1.

Mhaskar, H. N., et al.. (2025). Robust and tractable multidimensional exponential analysis. Numerical Algorithms.

2.

Mhaskar, H. N., et al.. (2024). Learning on manifolds without manifold learning. Neural Networks. 181. 106759–106759. 1 indexed citations

3.

Mhaskar, H. N., et al.. (2024). Tractability of approximation by general shallow networks. Analysis and Applications. 22(3). 535–568. 2 indexed citations

4.

Mhaskar, H. N., et al.. (2022). A manifold learning approach for gesture recognition from micro-Doppler radar measurements. Neural Networks. 152. 353–369. 2 indexed citations

5.

Mhaskar, H. N.. (2019). Deep Gaussian networks for function approximation on data defined manifolds.. arXiv (Cornell University). 1 indexed citations

6.

Poggio, Tomaso, H. N. Mhaskar, Lorenzo Rosasco, Brando Miranda, & Qianli Liao. (2016). Why and When Can Deep -- but Not Shallow -- Networks Avoid the Curse of\n Dimensionality: a Review. arXiv (Cornell University). 5 indexed citations

7.

Poggio, Tomaso, H. N. Mhaskar, Lorenzo Rosasco, Brando Miranda, & Qianli Liao. (2016). Why and When Can Deep -- but Not Shallow -- Networks Avoid the Curse of Dimensionality. arXiv (Cornell University). 3 indexed citations

8.

Jetter, Κ., et al.. (2015). In Memoriam: Hubert A. Berens (1936–2015). Journal of Approximation Theory. 198. 4–24. 1 indexed citations

9.

Chui, Charles K., Frank Filbir, & H. N. Mhaskar. (2014). Representation of functions on big data: Graphs and trees. Applied and Computational Harmonic Analysis. 38(3). 489–509. 16 indexed citations

10.

Mhaskar, H. N.. (2005). On the representation of smooth functions on the sphere using finitely many bits. Applied and Computational Harmonic Analysis. 18(3). 215–233. 19 indexed citations

11.

Jain, Pooja, H. N. Mhaskar, & Murali C. Krishna. (2001). Wavelets and allied topics. CERN Document Server (European Organization for Nuclear Research). 5 indexed citations

12.

Mhaskar, H. N. & Jürgen Prestin. (1999). Bounded Quasi-Interpolatory Polynomial Operators. Journal of Approximation Theory. 96(1). 67–85. 6 indexed citations

13.

Mhaskar, H. N. & Jürgen Prestin. (1997). On Marcinkiewicz-Zygmund-Type Inequalities. 2 indexed citations

14.

Mhaskar, H. N. & Charles A. Micchelli. (1995). Degree of Approximation by Neural and Translation Networks with a Single Hidden Layer. Advances in Applied Mathematics. 16(2). 151–183. 96 indexed citations

15.

Chui, Charles K., Xin Li, & H. N. Mhaskar. (1994). Neural networks for localized approximation. Mathematics of Computation. 63(208). 607–607. 63 indexed citations

16.

Chui, Charles K., Xin Li, & H. N. Mhaskar. (1994). Neural Networks for Localized Approximation. Mathematics of Computation. 63(208). 607–607. 51 indexed citations

17.

Mhaskar, H. N. & Charles A. Micchelli. (1993). How to Choose an Activation Function. Neural Information Processing Systems. 6. 319–326. 30 indexed citations

18.

Mhaskar, H. N., et al.. (1992). Detection of singularities using segment approximation. Mathematics of Computation. 59(200). 533–540. 2 indexed citations

19.

Mhaskar, H. N.. (1991). The convergence of Fourier series and a k-functional. Journal of Mathematical Analysis and Applications. 154(1). 134–141.

20.

Mhaskar, H. N.. (1980). Weighted polynomial approximation on the whole real line and related topics /. OhioLink ETD Center (Ohio Library and Information Network). 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.

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