Brando Miranda

1.2k total citations · 1 hit paper
12 papers, 529 citations indexed

About

Brando Miranda is a scholar working on Artificial Intelligence, Atomic and Molecular Physics, and Optics and Computational Mechanics. According to data from OpenAlex, Brando Miranda has authored 12 papers receiving a total of 529 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 3 papers in Atomic and Molecular Physics, and Optics and 3 papers in Computational Mechanics. Recurrent topics in Brando Miranda's work include Neural Networks and Applications (4 papers), Stochastic Gradient Optimization Techniques (4 papers) and Sparse and Compressive Sensing Techniques (3 papers). Brando Miranda is often cited by papers focused on Neural Networks and Applications (4 papers), Stochastic Gradient Optimization Techniques (4 papers) and Sparse and Compressive Sensing Techniques (3 papers). Brando Miranda collaborates with scholars based in United States. Brando Miranda's co-authors include Lorenzo Rosasco, Tomaso Poggio, Qianli Liao, H. N. Mhaskar, Derek Kita, Hongtao Lin, Juejun Hu, David Bono, Jérôme Michon and Tian Gu and has published in prestigious journals such as Nature Communications, International Journal of Automation and Computing and arXiv (Cornell University).

In The Last Decade

Brando Miranda

11 papers receiving 506 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. International Journal of Automation and Computing profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Brando Miranda United States	4	197	188	104	82	73	12	529
Miao Hu China	13	450 2.3×	67 0.4×	224 2.2×	78 1.0×	132 1.8×	110	686
Yuqi Li China	16	63 0.3×	94 0.5×	86 0.8×	97 1.2×	87 1.2×	82	693
Arnab Ghosh India	13	223 1.1×	148 0.8×	208 2.0×	209 2.5×	33 0.5×	42	609
Rafael Guzmán-Cabrera Mexico	11	109 0.6×	197 1.0×	24 0.2×	39 0.5×	56 0.8×	72	596
Guoqiang Han China	12	120 0.6×	108 0.6×	80 0.8×	13 0.2×	100 1.4×	33	467
S. H. Oh South Korea	12	130 0.7×	107 0.6×	43 0.4×	21 0.3×	93 1.3×	47	545
Daniel Ioan Romania	8	252 1.3×	142 0.8×	43 0.4×	46 0.6×	74 1.0×	64	545
Said Mikki United States	19	857 4.4×	185 1.0×	217 2.1×	44 0.5×	109 1.5×	140	1.4k
Yipeng Ding China	16	142 0.7×	73 0.4×	59 0.6×	123 1.5×	214 2.9×	72	624
Lijun Qiao China	15	532 2.7×	123 0.7×	282 2.7×	127 1.5×	112 1.5×	57	764

Countries citing papers authored by Brando Miranda

Since Specialization

Citations

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

Fields of papers citing papers by Brando Miranda

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Brando Miranda

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

All Works

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12 of 12 papers shown

1.

Miranda, Brando. (2020). An empirical study of the properties of meta-learning - presentation. IDEALS (University of Illinois Urbana-Champaign).

2.

Liao, Qianli, et al.. (2019). Theory III: Dynamics and Generalization in Deep Networks -- a simple solution. arXiv (Cornell University). 2 indexed citations

3.

Kita, Derek, Brando Miranda, Carlos Rı́os, et al.. (2019). Chip-scale high-performance digital Fourier Transform (dFT) spectrometers. DSpace@MIT (Massachusetts Institute of Technology). 2–2. 2 indexed citations

4.

Liao, Qianli, Brando Miranda, Lorenzo Rosasco, et al.. (2019). Generalization Puzzles in Deep Networks. 1 indexed citations

5.

Kita, Derek, Brando Miranda, David Bono, et al.. (2018). High-performance and scalable on-chip digital Fourier transform spectroscopy. Nature Communications. 9(1). 4405–4405. 204 indexed citations

6.

Kita, Derek, Brando Miranda, David Bono, et al.. (2018). High-resolution on-chip digital Fourier transform spectroscopy. Conference on Lasers and Electro-Optics. SF1A.1–SF1A.1. 3 indexed citations

7.

Poggio, Tomaso, H. N. Mhaskar, Lorenzo Rosasco, Brando Miranda, & Qianli Liao. (2017). Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review. International Journal of Automation and Computing. 14(5). 503–519. 304 indexed citations breakdown →

8.

Zhang, Chiyuan, Qianli Liao, Alexander Rakhlin, et al.. (2017). Musings on Deep Learning: Properties of SGD. DSpace@MIT (Massachusetts Institute of Technology). 1 indexed citations

9.

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

10.

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

11.

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: a Review. arXiv (Cornell University). 3 indexed citations

12.

Poggio, Tomaso, H. N. Mhaskar, Lorenzo Rosasco, Brando Miranda, & Qianli Liao. (2016). Theory I: Why and When Can Deep Networks Avoid the Curse of Dimensionality?. DSpace@MIT (Massachusetts Institute of Technology). 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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