David Côté

579 citations
13 papers · 314 · 1 hit paper · h-index 7

Impact in

Papers in

David Côté

12 papers receiving 300 citations

David Côté's Hit Papers

SAITS: Self-attention-based imputation for time series 2023 · 175 citations
1750+1+2Years since publication50100150

Peers

David Côté
Comparison fields: 5 of 78
  • Signal Processing 71
  • Artificial Intelligence 115
  • Computational Mathematics 2
  • Computer Vision and Pattern Recognition 46
  • Computer Networks and Communications 51
Replace Sunitha Basodi with:
Sunitha Basodi United States
Daw-Ran Liou Taiwan
S. Santhosh Baboo India
Md. Nasim Adnan Australia
Shariq Bashir Pakistan
Syam Machinathu Parambil Gangadharan India
David Côté relative to Sunitha Basodi United States Sunitha Basodi's profile →
Citations per field
00.5×1.5×
Sunitha Basodi · 1×
Citations per year

Countries citing papers authored by David Côté

Since Specialization
Citations

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

Fields of papers citing papers by David Côté

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 14 scholars most cited alongside David Côté, 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 David Côté Line = papers co-authored together David Côté links everyone, so they are left out of the graph.

All Works

13 of 13 papers shown
#Work
1
SAITS: Self-attention-based imputation for time series
Hit paper breakdown →
2023175
2 201858
3 202117
4 202113
5 202111
6 201910
7 20208
8 20236
9 20216
10 20225
11 20234
12 20221
13 20230

About David Côté

David Côté is a scholar working on Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering, Signal Processing and Computer Vision and Pattern Recognition, having authored 13 papers that have together received 314 indexed citations. Recurring topics across this work include Network Security and Intrusion Detection (5 papers), Optical Network Technologies (4 papers), Internet Traffic Analysis and Secure E-voting (4 papers), Software System Performance and Reliability (3 papers), Image and Video Quality Assessment (2 papers), Software-Defined Networks and 5G (2 papers), Advanced Optical Network Technologies (1 paper) and Coral and Marine Ecosystems Studies (1 paper). The work is most often cited by research in Signal Processing (71 citations), Artificial Intelligence (115 citations), Computational Mathematics (2 citations), Computer Vision and Pattern Recognition (46 citations) and Computer Networks and Communications (51 citations). David Côté has collaborated with scholars based in Canada, United States and Portugal. Frequent co-authors include Yan Liu, Wenjie Du, Shervin Shirmohammadi, Michael E. Reimer, Chris Barber, Craig J. Brown, Ryan R. E. Stanley, Corey J. Morris, Thomas S. Bianchi and Paul V. R. Snelgrove. Their work appears in journals such as Journal of Optical Communications and Networking, Ecology and Society, IEEE Transactions on Instrumentation and Measurement, IEEE Transactions on Network and Service Management and Expert Systems with Applications.

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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