| Multi-column deep neural networks for image classification D Ciregan, U Meier, J Schmidhuber 2012 IEEE conference on computer vision and pattern recognition, 3642-3649, 2012 | 7689* | 2012 |
| Stacked convolutional auto-encoders for hierarchical feature extraction J Masci, U Meier, D Cireşan, J Schmidhuber Artificial Neural Networks and Machine Learning–ICANN 2011: 21st …, 2011 | 2760 | 2011 |
| 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 | 2314 | 2017 |
| Flexible, high performance convolutional neural networks for image classification DC Ciresan, U Meier, J Masci, LM Gambardella, J Schmidhuber Twenty-second international joint conference on artificial intelligence, 2011 | 2258 | 2011 |
| Geodesic convolutional neural networks on riemannian manifolds J Masci, D Boscaini, M Bronstein, P Vandergheynst Proceedings of the IEEE international conference on computer vision …, 2015 | 926 | 2015 |
| Learning shape correspondence with anisotropic convolutional neural networks D Boscaini, J Masci, E Rodolà, M Bronstein Advances in neural information processing systems 29, 2016 | 623 | 2016 |
| Fast image scanning with deep max-pooling convolutional neural networks A Giusti, DC Cireşan, J Masci, LM Gambardella, J Schmidhuber 2013 IEEE international conference on image processing, 4034-4038, 2013 | 448 | 2013 |
| High-performance neural networks for visual object classification DC Cireşan, U Meier, J Masci, LM Gambardella, J Schmidhuber arXiv preprint arXiv:1102.0183, 2011 | 420 | 2011 |
| Steel defect classification with max-pooling convolutional neural networks J Masci, U Meier, D Ciresan, J Schmidhuber, G Fricout The 2012 international joint conference on neural networks (IJCNN), 1-6, 2012 | 414 | 2012 |
| Deep networks with internal selective attention through feedback connections MF Stollenga, J Masci, F Gomez, J Schmidhuber Advances in neural information processing systems 27, 2014 | 335 | 2014 |
| Learning to detect objects with a 1 megapixel event camera E Perot, P De Tournemire, D Nitti, J Masci, A Sironi Advances in Neural Information Processing Systems 33, 16639-16652, 2020 | 264 | 2020 |
| Learning classspecific descriptors for deformable shapes using localized spectral convolutional networks D Boscaini, J Masci, S Melzi, MM Bronstein, U Castellani, ... Computer graphics forum 34 (5), 13-23, 2015 | 255 | 2015 |
| Compete to compute RK Srivastava, J Masci, S Kazerounian, F Gomez, J Schmidhuber Advances in neural information processing systems 26, 2013 | 247 | 2013 |
| Deep graph matching consensus M Fey, JE Lenssen, C Morris, J Masci, NM Kriege arXiv preprint arXiv:2001.09621, 2020 | 246 | 2020 |
| Multimodal similarity-preserving hashing J Masci, MM Bronstein, AM Bronstein, J Schmidhuber IEEE transactions on pattern analysis and machine intelligence 36 (4), 824-830, 2013 | 228 | 2013 |
| Anisotropic diffusion descriptors D Boscaini, J Masci, E Rodolà, MM Bronstein, D Cremers Computer Graphics Forum 35 (2), 431-441, 2016 | 149 | 2016 |
| Two-stage peer-regularized feature recombination for arbitrary image style transfer J Svoboda, A Anoosheh, C Osendorfer, J Masci Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 88 | 2020 |
| A fast learning algorithm for image segmentation with max-pooling convolutional networks J Masci, A Giusti, D Ciresan, G Fricout, J Schmidhuber 2013 IEEE international conference on image processing, 2713-2717, 2013 | 88 | 2013 |
| Matching deformable objects in clutter L Cosmo, E Rodola, J Masci, A Torsello, MM Bronstein 2016 Fourth international conference on 3D vision (3DV), 1-10, 2016 | 74 | 2016 |
| Shapenet: Convolutional neural networks on non-euclidean manifolds J Masci, D Boscaini, M Bronstein, P Vandergheynst | 69 | 2015 |
Juergen SchmidhuberKing Abdullah University of Science and Technology / The Swiss AI Lab, IDSIA / University of Lugano在 kaust.edu.sa 的电子邮件经过验证
