Hanlin Tang

2.7k citations
37 papers · 620 · h-index 11

Impact in

Papers in

Hanlin Tang

33 papers receiving 607 citations

Peers

Hanlin Tang
Comparison fields: 5 of 120
  • Cognitive Neuroscience 213
  • Computer Vision and Pattern Recognition 169
  • Artificial Intelligence 121
  • Hardware and Architecture 24
  • Ecological Modeling 11
Replace Moritz B. Milde with:
Moritz B. Milde Germany
Wolfram Schenck Germany
Aleksandar Zlateski United States
Matthew L. Leavitt Canada
Xavier Lagorce France
Nitin Rathi United States
Eva L. Dyer United States
Aaron R. Voelker Canada
Андерсон United States
Cong Shi China
Hanlin Tang relative to Moritz B. Milde Germany Moritz B. Milde's profile →
Citations per field
00.5×3.7×
Moritz B. Milde · 1×
Citations per year

Countries citing papers authored by Hanlin Tang

Since Specialization
Citations

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

Fields of papers citing papers by Hanlin Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 37 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018109
2 202090
3 201462
4 202355
5 202052
6 201949
7 201939
8 201631
9 201219
10
Amur Tiger Re-identification in the Wild.
201914
11 201511
12 201610
13 202310
14 201810
15 20229
16 20208
17 20216
18 20196
19
Decentralization Meets Quantization.
20185
20 20233

About Hanlin Tang

Hanlin Tang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Cognitive Neuroscience, Electrical and Electronic Engineering and Computer Networks and Communications, having authored 37 papers that have together received 620 indexed citations. Recurring topics across this work include Neural dynamics and brain function (8 papers), Memory and Neural Mechanisms (5 papers), Stochastic Gradient Optimization Techniques (4 papers), Face Recognition and Perception (3 papers), Advanced Neural Network Applications (3 papers), Visual perception and processing mechanisms (3 papers), Privacy-Preserving Technologies in Data (3 papers) and Natural Language Processing Techniques (3 papers). The work is most often cited by research in Cognitive Neuroscience (213 citations), Computer Vision and Pattern Recognition (169 citations), Artificial Intelligence (121 citations), Hardware and Architecture (24 citations) and Ecological Modeling (11 citations). Hanlin Tang has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Gabriel Kreiman, Joseph R. Madsen, Nathan E. Crone, William S. Anderson, Weiyao Lin, David Cox, Charlotte Moerman, Ana Paredes, William Lotter and Martin Schrimpf. Their work appears in journals such as Scientific Reports, Computers in Industry, Journal of Neurophysiology, Frontiers in Immunology and Computer Communications.

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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