José Cano

34 papers receiving 186 citations

Peers

José Cano
Comparison fields: 5 of 40
  • Computational Mathematics 5
  • Hardware and Architecture 38
  • Computer Vision and Pattern Recognition 70
  • Computer Networks and Communications 65
  • Artificial Intelligence 90
Replace Shaofeng H.-C. Jiang with:
Shaofeng H.-C. Jiang China
Mohammad Sadegh Talebi Iran
Alan Soper United Kingdom
Nikita Mishra United States
Douglas Aberdeen Australia
Yang Su China
L. S. S. Reddy India
Rajendra Kumar India
Fábio Borges Brazil
Jorge M. Cortés-Mendoza Russia
José Cano relative to Shaofeng H.-C. Jiang China Shaofeng H.-C. Jiang's profile →
Citations per field
00.5×1.5×
Shaofeng H.-C. Jiang · 1×
Citations per year

Countries citing papers authored by José Cano

Since Specialization
Citations

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

Fields of papers citing papers by José Cano

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside José Cano, 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 José Cano Line = papers co-authored together José Cano links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 36 papers — load more, or switch the sort, to bring in the rest.

#Work
1 199337
2 201925
3 201818
4
Accelerating Deep Neural Networks on Low Power Heterogeneous Architectures
201812
5 201210
6 20189
7 20208
8 20247
9 20187
10 20227
11 20216
12 20106
13 20225
14 20155
15 20164
16 20164
17 20224
18 20064
19 20233
20 20132

About José Cano

José Cano is a scholar working on Computer Networks and Communications, Computer Vision and Pattern Recognition, Artificial Intelligence, Hardware and Architecture and Electrical and Electronic Engineering, having authored 36 papers that have together received 202 indexed citations. Recurring topics across this work include Parallel Computing and Optimization Techniques (11 papers), Advanced Neural Network Applications (11 papers), Adversarial Robustness in Machine Learning (7 papers), Interconnection Networks and Systems (5 papers), Domain Adaptation and Few-Shot Learning (4 papers), Embedded Systems Design Techniques (4 papers), Advanced Memory and Neural Computing (4 papers) and Mobile Ad Hoc Networks (3 papers). The work is most often cited by research in Computational Mathematics (5 citations), Hardware and Architecture (38 citations), Computer Vision and Pattern Recognition (70 citations), Computer Networks and Communications (65 citations) and Artificial Intelligence (90 citations). José Cano has collaborated with scholars based in United Kingdom, Spain and United States. Frequent co-authors include Miguel Delgado, Serafı́n Moral, Michael O’Boyle, Valentin Radu, Jack Turner, Elliot J. Crowley, Amos Storkey, Vijay Nagarajan, Michael O’Boyle and José Flich. Their work appears in journals such as Journal of Network and Computer Applications, IEEE Transactions on Parallel and Distributed Systems, International Journal of Approximate Reasoning, IEEE Communications Magazine and IEEE Transactions on Computers.

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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