Jonathan Masci

7.0k citations
28 papers · 4.6k · 3 hit papers · h-index 18

Impact in

Papers in

Jonathan Masci

27 papers receiving 4.4k citations

Jonathan Masci's Hit Papers

Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs 2017 · 1.2k citations
1.2k0+5+10Years since publication2505007501000

Peers

Jonathan Masci
Comparison fields: 5 of 169
  • Computer Vision and Pattern Recognition 2.4k
  • Computer Graphics and Computer-Aided Design 229
  • Media Technology 425
  • Artificial Intelligence 1.4k
  • Geology 205
Replace Xiaonan Luo with:
Xiaonan Luo China
Samuel Rota Bulò Italy
Xiao Bai China
Longin Jan Latecki United States
Jiquan Ngiam United States
Sicheng Zhao China
Jan Puzicha Germany
Song Bai China
Fan Zhu China
Mingli Song China
Jonathan Masci relative to Xiaonan Luo China Xiaonan Luo's profile →
Citations per field
00.5×2×3.3×
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Citations per year

Countries citing papers authored by Jonathan Masci

Since Specialization
Citations

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

Fields of papers citing papers by Jonathan Masci

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs
Hit paper breakdown →
20171198
2
Flexible, high performance convolutional neural networks for image classification
Hit paper breakdown →
2011933
3
Multi-column deep neural network for traffic sign classification
Hit paper breakdown →
2012823
4 2011316
5 2012269
6 2013229
7 2015157
8 2014150
9 2016100
10 201561
11 202057
12 201348
13 201647
14 201646
15 201342
16 201633
17
ShapeNet: Convolutional Neural Networks on Non-Euclidean Manifolds
201524
18 201323
19 201113
20
Learning to Detect Objects with a 1 Megapixel Event Camera
202010

About Jonathan Masci

Jonathan Masci is a scholar working on Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering, Computational Mechanics, Human-Computer Interaction and Computer Graphics and Computer-Aided Design, having authored 28 papers that have together received 4.6k indexed citations. Recurring topics across this work include 3D Shape Modeling and Analysis (8 papers), Advanced Neural Network Applications (7 papers), Advanced Image and Video Retrieval Techniques (7 papers), Industrial Vision Systems and Defect Detection (5 papers), Advanced Vision and Imaging (3 papers), Image Processing and 3D Reconstruction (3 papers), Medical Imaging and Analysis (3 papers) and Hand Gesture Recognition Systems (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (2.4k citations), Computer Graphics and Computer-Aided Design (229 citations), Media Technology (425 citations), Artificial Intelligence (1.4k citations) and Geology (205 citations). Jonathan Masci has collaborated with scholars based in Switzerland, Italy and France. Frequent co-authors include Jürgen Schmidhuber, Dan Cireşan, Ueli Meier, Michael M. Bronstein, Davide Boscaini, Emanuele Rodolà, Luca Maria Gambardella, Jan Svoboda, Federico Monti and Gabriel Fricout. Their work appears in journals such as Computer Graphics Forum, Medical Image Analysis, Neural Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence and Lecture notes in computer science.

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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