Emanuele Rodolà

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Michael BronsteinDeepMind Professor of AI, University of Oxford / Scientific Director, AITHYRAVerified email at cs.ox.ac.uk
Andrea TORSELLOProfessor of Computer Science, Università Ca' Foscari VeneziaVerified email at unive.it
Daniel CremersTechnical University of MunichVerified email at tum.de
Luca CosmoCa' Foscari University of VeniceVerified email at unive.it
Andrea AlbarelliUniversità Ca' Foscari di VeneziaVerified email at albarelli.it
Simone MelziAssistant Professor, Computer Science, University of Milano-BicoccaVerified email at unimib.it
Alex BronsteinDan Broida Academic Chair, Schmidt Chair of AI, CS@TechnionVerified email at technion.ac.il
Davide BoscainiResearcher, Fondazione Bruno KesslerVerified email at fbk.eu
Or LitanyTechnion, NVIDIAVerified email at nvidia.com
Maks OvsjanikovProfessor, Ecole PolytechniqueVerified email at lix.polytechnique.fr
Jonathan MasciNNAISENSE SAVerified email at nnaisense.com
Riccardo MarinUniversity of TübingenVerified email at mnf.uni-tuebingen.de
Federico MontiUniversità della Svizzera italianaVerified email at usi.ch
Luca MoschellaAppleVerified email at apple.com
Marco FumeroPostDoctoral Researcher, Institute of Science and Technology Austria (ISTA)Verified email at ist.ac.at
Filippo BERGAMASCOUniversità Ca'Foscari di VeneziaVerified email at unive.it
Umberto CastellaniDipartimento di Informatica, University of VeronaVerified email at univr.it
Matthias VestnerComputer Vision Group, Technische Universität MünchenVerified email at in.tum.de
Valentino MaiorcaPh.D. student in Computer Science, Sapienza UniversityVerified email at di.uniroma1.it
Zorah LähnerAssistant Professor, University of BonnVerified email at uni-bonn.de

Emanuele Rodolà

Professor of Computer Science, Sapienza University of Rome

Verified email at di.uniroma1.it - Homepage

Machine Learning Audio Geometric Deep Learning Geometry Processing Computer Vision


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Geometric deep learning on graphs and manifolds using mixture model cnns F Monti, D Boscaini, J Masci, E Rodola, J Svoboda, MM Bronstein Proceedings of the IEEE conference on computer vision and pattern …, 2017	2262	2017
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... arXiv preprint arXiv:2206.04615, 2022	1000	2022
Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning P Gainza, F Sverrisson, F Monti, E Rodola, D Boscaini, MM Bronstein, ... Nature Methods 17 (2), 184-192, 2020	622	2020
Learning shape correspondence with anisotropic convolutional neural networks D Boscaini, J Masci, E Rodolà, M Bronstein Advances in neural information processing systems 29, 2016	619	2016
Deep functional maps: Structured prediction for dense shape correspondence O Litany, T Remez, E Rodola, A Bronstein, M Bronstein Proceedings of the IEEE international conference on computer vision, 5659-5667, 2017	313	2017
Partial functional correspondence E Rodolà, L Cosmo, MM Bronstein, A Torsello, D Cremers Computer graphics forum 36 (1), 222-236, 2017	286	2017
Dense Non-Rigid Shape Correspondence using Random Forests E Rodolà, S Rota Bulò, T Windheuser, M Vestner, D Cremers Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, 2014	191	2014
Zoomout: Spectral upsampling for efficient shape correspondence S Melzi, J Ren, E Rodola, A Sharma, P Wonka, M Ovsjanikov arXiv preprint arXiv:1904.07865, 2019	190	2019
RUNE-Tag: A high accuracy fiducial marker with strong occlusion resilience F Bergamasco, A Albarelli, E Rodolà, A Torsello Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, 113-120, 2011	187	2011
Unsupervised learning of dense shape correspondence O Halimi, O Litany, E Rodola, AM Bronstein, R Kimmel Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019	182	2019
Anisotropic diffusion descriptors D Boscaini, J Masci, E Rodolà, MM Bronstein, D Cremers Computer Graphics Forum 35 (2), 431-441, 2016	148	2016
Computing and processing correspondences with functional maps M Ovsjanikov, E Corman, M Bronstein, E Rodola, M Ben-Chen, L Guibas, ... ACM SIGGRAPH 2017 Courses, 1-62, 2017	144	2017
Computing and processing correspondences with functional maps M Ovsjanikov, E Corman, M Bronstein, E Rodolà, M Ben-Chen, L Guibas, ... SIGGRAPH ASIA 2016 Courses, 9, 2016	144	2016
Product manifold filter: Non-rigid shape correspondence via kernel density estimation in the product space M Vestner, R Litman, E Rodola, A Bronstein, D Cremers Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017	137	2017
A scale independent selection process for 3d object recognition in cluttered scenes E Rodola, A Albarelli, F Bergamasco, A Torsello International journal of computer vision 102, 129-145, 2013	131	2013
Multiview registration via graph diffusion of dual quaternions A Torsello, E Rodolà, A Albarelli Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on …, 2011	127	2011
Fully spectral partial shape matching O Litany, E Rodolà, AM Bronstein, MM Bronstein Computer Graphics Forum 36 (2), 247-258, 2017	124	2017
A Game-Theoretic Approach to Deformable Shape Matching E Rodolà, AM Bronstein, A Albarelli, F Bergamasco, A Torsello Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, 2012	105	2012
Efficient deformable shape correspondence via kernel matching M Vestner, Z Lähner, A Boyarski, O Litany, R Slossberg, T Remez, ... 2017 international conference on 3D vision (3DV), 517-526, 2017	102*	2017
Cluster-driven graph federated learning over multiple domains D Caldarola, M Mancini, F Galasso, M Ciccone, E Rodolà, B Caputo Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021	92	2021

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