| 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 | 2181 | 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 | 817 | 2022 |
| Learning shape correspondence with anisotropic convolutional neural networks D Boscaini, J Masci, E Rodolà, M Bronstein Advances in neural information processing systems 29, 2016 | 607 | 2016 |
| 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 | 579 | 2020 |
| 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 | 299 | 2017 |
| Partial functional correspondence E Rodolà, L Cosmo, MM Bronstein, A Torsello, D Cremers Computer graphics forum 36 (1), 222-236, 2017 | 274 | 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 | 188 | 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 | 176 | 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 | 175 | 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 | 171 | 2019 |
| Anisotropic diffusion descriptors D Boscaini, J Masci, E Rodolà, MM Bronstein, D Cremers Computer Graphics Forum 35 (2), 431-441, 2016 | 146 | 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 | 136 | 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 | 136 | 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 | 135 | 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 | 129 | 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 | 124 | 2011 |
| Fully spectral partial shape matching O Litany, E Rodolà, AM Bronstein, MM Bronstein Computer Graphics Forum 36 (2), 247-258, 2017 | 122 | 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 | 102 | 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 | 99* | 2017 |
| Non‐rigid puzzles O Litany, E Rodolà, AM Bronstein, MM Bronstein, D Cremers Computer Graphics Forum 35 (5), 135-143, 2016 | 89 | 2016 |
Michael BronsteinDeepMind Professor of AI, University of Oxford在 cs.ox.ac.uk 的电子邮件经过验证
