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.
Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs
2017981 citationsFederico Monti, Davide Boscaini et al.IRIS Research product catalog (Sapienza University of Rome)profile →
Flexible, high performance convolutional neural networks for image classification
2011812 citationsDan Cireşan, Ueli Meier et al.International Joint Conference on Artificial Intelligenceprofile →
Multi-column deep neural network for traffic sign classification
2012691 citationsDan Cireşan, Ueli Meier et al.Neural Networksprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
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).
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-authorship network of co-authors of Jonathan Masci
This figure shows the co-authorship network connecting the top 25 collaborators of Jonathan Masci.
A scholar is included among the top collaborators of Jonathan Masci 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 Jonathan Masci. Jonathan Masci is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Pérot, Etienne, et al.. (2020). Learning to Detect Objects with a 1 Megapixel Event Camera. Neural Information Processing Systems. 33. 16639–16652.7 indexed citations
Masci, Jonathan, et al.. (2019). Accelerating Neural ODEs with Spectral Elements.. arXiv (Cornell University).5 indexed citations
6.
Lenssen, Jan Eric, Christian Osendorfer, & Jonathan Masci. (2019). Differentiable Iterative Surface Normal Estimation.. arXiv (Cornell University).2 indexed citations
Ciccone, Marco, et al.. (2018). NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 31. 3025–3035.4 indexed citations
9.
Monti, Federico, Davide Boscaini, Jonathan Masci, et al.. (2017). Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs. IRIS Research product catalog (Sapienza University of Rome). 5425–5434.981 indexed citations breakdown →
10.
Masci, Jonathan, Emanuele Rodolà, Davide Boscaini, Michael M. Bronstein, & Hao Li. (2016). Geometric deep learning. IRIS Research product catalog (Sapienza University of Rome). 1–50.27 indexed citations
11.
Cosmo, Luca, Emanuele Rodolà, Jonathan Masci, Andrea Torsello, & Michael M. Bronstein. (2016). Matching Deformable Objects in Clutter. IRIS Research product catalog (Sapienza University of Rome). 1–10.31 indexed citations
12.
Boscaini, Davide, Jonathan Masci, Emanuele Rodolà, Michael M. Bronstein, & Daniel Cremers. (2016). Anisotropic Diffusion Descriptors. Computer Graphics Forum. 35(2). 431–441.81 indexed citations
Masci, Jonathan. (2014). Advances in deep learning for vision, with applications to industrial inspection.1 indexed citations
15.
Masci, Jonathan, Michael M. Bronstein, Alexander M. Bronstein, & Jürgen Schmidhuber. (2014). Multimodal Similarity-Preserving Hashing. IEEE Transactions on Pattern Analysis and Machine Intelligence. 36(4). 824–830.136 indexed citations
Cireşan, Dan, Ueli Meier, Jonathan Masci, & Jürgen Schmidhuber. (2012). Multi-column deep neural network for traffic sign classification. Neural Networks. 32. 333–338.691 indexed citations breakdown →
19.
Cireşan, Dan, Ueli Meier, Jonathan Masci, Luca Maria Gambardella, & Jürgen Schmidhuber. (2011). Flexible, high performance convolutional neural networks for image classification. International Joint Conference on Artificial Intelligence. 1237–1242.812 indexed citations breakdown →
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.