| Stacked convolutional auto-encoders for hierarchical feature extraction J Masci, U Meier, D Cireşan, J Schmidhuber International conference on artificial neural networks, 52-59, 2011 | 1251 | 2011 |
| 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 | 1232 | 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 | 666 | 2017 |
| 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 | 436 | 2015 |
| A committee of neural networks for traffic sign classification D Cireşan, U Meier, J Masci, J Schmidhuber The 2011 international joint conference on neural networks, 1918-1921, 2011 | 392 | 2011 |
| Learning shape correspondence with anisotropic convolutional neural networks D Boscaini, J Masci, E Rodolà, M Bronstein Advances in neural information processing systems, 3189-3197, 2016 | 307 | 2016 |
| High-performance neural networks for visual object classification DC Cireşan, U Meier, J Masci, LM Gambardella, J Schmidhuber arXiv preprint arXiv:1102.0183, 2011 | 251 | 2011 |
| 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 | 250 | 2013 |
| Deep networks with internal selective attention through feedback connections MF Stollenga, J Masci, F Gomez, J Schmidhuber Advances in neural information processing systems, 3545-3553, 2014 | 202 | 2014 |
| 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 | 184 | 2013 |
| 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 | 174 | 2012 |
| Learning class‐specific 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 | 165 | 2015 |
| Compete to compute RK Srivastava, J Masci, S Kazerounian, F Gomez, J Schmidhuber Advances in neural information processing systems, 2310-2318, 2013 | 152 | 2013 |
| Anisotropic diffusion descriptors D Boscaini, J Masci, E Rodolà, MM Bronstein, D Cremers Computer Graphics Forum 35 (2), 431-441, 2016 | 93 | 2016 |
| 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 | 62 | 2013 |
| Shapenet: Convolutional neural networks on non-euclidean manifolds J Masci, D Boscaini, M Bronstein, P Vandergheynst | 42 | 2015 |
| Understanding locally competitive networks RK Srivastava, J Masci, F Gomez, J Schmidhuber arXiv preprint arXiv:1410.1165, 2014 | 41 | 2014 |
| 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 | 35 | 2016 |
| Geometric deep learning J Masci, E Rodolà, D Boscaini, MM Bronstein, H Li SIGGRAPH ASIA 2016 Courses, 1-50, 2016 | 29 | 2016 |
| Nais-net: Stable deep networks from non-autonomous differential equations M Ciccone, M Gallieri, J Masci, C Osendorfer, F Gomez Advances in Neural Information Processing Systems, 3025-3035, 2018 | 28 | 2018 |
Juergen SchmidhuberThe Swiss AI Lab IDSIA / USI & SUPSIVerified email at idsia.ch
