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.
SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
20126.4k citationsAurélien Lucchi, Pascal Fua et al.profile →
Quantum Generative Adversarial Networks for learning and loading random distributions
2019272 citationsChrista Zoufal, Aurélien Lucchi et al.Repository for Publications and Research Data (ETH Zurich)profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Aurélien Lucchi
Since
Specialization
Citations
This map shows the geographic impact of Aurélien Lucchi'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 Aurélien Lucchi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aurélien Lucchi more than expected).
This network shows the impact of papers produced by Aurélien Lucchi. 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 Aurélien Lucchi. The network helps show where Aurélien Lucchi may publish in the future.
Co-authorship network of co-authors of Aurélien Lucchi
This figure shows the co-authorship network connecting the top 25 collaborators of Aurélien Lucchi.
A scholar is included among the top collaborators of Aurélien Lucchi 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 Aurélien Lucchi. Aurélien Lucchi is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Bécigneul, Gary, et al.. (2021). Momentum Improves Optimization on Riemannian Manifolds. International Conference on Artificial Intelligence and Statistics. 1351–1359.1 indexed citations
Pavllo, Dario, et al.. (2020). Convolutional Generation of Textured 3D Meshes. Lirias (KU Leuven). 33. 870–882.1 indexed citations
7.
Bécigneul, Gary, et al.. (2020). Practical Accelerated Optimization on Riemannian Manifolds. arXiv (Cornell University).5 indexed citations
8.
Bécigneul, Gary, et al.. (2020). A Continuous-time Perspective for Modeling Acceleration in Riemannian Optimization.. International Conference on Artificial Intelligence and Statistics. 1297–1307.4 indexed citations
9.
Daneshmand, Hadi, Jonas Köhler, Francis Bach, Thomas Hofmann, & Aurélien Lucchi. (2020). Batch normalization provably avoids ranks collapse for randomly initialised deep networks. Neural Information Processing Systems. 33. 18387–18398.8 indexed citations
10.
Köhler, Jonas, et al.. (2019). Ellipsoidal Trust Region Methods and the Marginal Value of Hessian Information for Neural Network Training.. arXiv (Cornell University).2 indexed citations
11.
Daneshmand, Hadi, Jonas Köhler, Aurélien Lucchi, & Thomas Hofmann. (2018). Escaping Saddles with Stochastic Gradients. International Conference on Machine Learning. 1155–1164.6 indexed citations
12.
Wegner, Jan Dirk, et al.. (2018). PolyMapper: Extracting City Maps using Polygons.. arXiv (Cornell University).7 indexed citations
Hofmann, Thomas, Aurélien Lucchi, & Brian McWilliams. (2015). Neighborhood Watch: Stochastic Gradient Descent with Neighbors. arXiv (Cornell University).1 indexed citations
19.
Sznitman, Raphael, Aurélien Lucchi, Peter I. Frazier, Bruno Jedynak, & Pascal Fua. (2013). An Optimal Policy for Target Localization with Application to Electron Microscopy. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 28(1). 1–9.16 indexed citations
20.
Zufferey, Guillaume, Patrick Jermann, Aurélien Lucchi, & Pierre Dillenbourg. (2009). TinkerSheets. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 377–384.36 indexed citations
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.