| Going deeper with convolutions C Szegedy, W Liu, Y Jia, P Sermanet, S Reed, D Anguelov, D Erhan, ... Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 65133 | 2015 |
| Ssd: Single shot multibox detector W Liu, D Anguelov, D Erhan, C Szegedy, S Reed, CY Fu, AC Berg Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016 | 43961 | 2016 |
| Intriguing properties of neural networks C Szegedy arXiv preprint arXiv:1312.6199, 2013 | 18763 | 2013 |
| Show and tell: A neural image caption generator O Vinyals, A Toshev, S Bengio, D Erhan Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 8085 | 2015 |
| Why does unsupervised pre-training help deep learning? D Erhan, A Courville, Y Bengio, P Vincent Proceedings of the thirteenth international conference on artificial …, 2010 | 3733 | 2010 |
| Challenges in representation learning: A report on three machine learning contests IJ Goodfellow, D Erhan, PL Carrier, A Courville, M Mirza, B Hamner, ... Neural information processing: 20th international conference, ICONIP 2013 …, 2013 | 2303 | 2013 |
| Deep neural networks for object detection C Szegedy, A Toshev, D Erhan Advances in neural information processing systems 26, 1-9, 2013 | 2172 | 2013 |
| Unsupervised pixel-level domain adaptation with generative adversarial networks K Bousmalis, N Silberman, D Dohan, D Erhan, D Krishnan Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 1994 | 2017 |
| Visualizing higher-layer features of a deep network D Erhan, Y Bengio, A Courville, P Vincent University of Montreal 1341 (3), 1, 2009 | 1855 | 2009 |
| European conference on computer vision W Liu, D Anguelov, D Erhan, C Szegedy, S Reed, CY Fu, AC Berg Face detection with end-to-end integration of a convnet and a 3d model, 2016 | 1849 | 2016 |
| Domain separation networks K Bousmalis, G Trigeorgis, N Silberman, D Krishnan, D Erhan Advances in neural information processing systems 29, 2016 | 1808 | 2016 |
| Scalable object detection using deep neural networks D Erhan, C Szegedy, A Toshev, D Anguelov Proceedings of the IEEE conference on computer vision and pattern …, 2014 | 1646 | 2014 |
| An empirical evaluation of deep architectures on problems with many factors of variation H Larochelle, D Erhan, A Courville, J Bergstra, Y Bengio Proceedings of the 24th international conference on Machine learning, 473-480, 2007 | 1456 | 2007 |
| Training deep neural networks on noisy labels with bootstrapping S Reed, H Lee, D Anguelov, C Szegedy, D Erhan, A Rabinovich arXiv preprint arXiv:1412.6596, 2014 | 1246 | 2014 |
| Show and tell: Lessons learned from the 2015 mscoco image captioning challenge O Vinyals, A Toshev, S Bengio, D Erhan IEEE transactions on pattern analysis and machine intelligence 39 (4), 652-663, 2016 | 1152 | 2016 |
| Theano: A Python framework for fast computation of mathematical expressions R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ... arXiv e-prints, arXiv: 1605.02688, 2016 | 1114* | 2016 |
| Model-based reinforcement learning for atari L Kaiser, M Babaeizadeh, P Milos, B Osinski, RH Campbell, ... arXiv preprint arXiv:1903.00374, 2019 | 1063 | 2019 |
| Proceedings of the IEEE conference on computer vision and pattern recognition C Szegedy, W Liu, Y Jia, P Sermanet, S Reed, D Anguelov, D Erhan, ... Going deeper with convolutions, 1-9, 2015 | 1038 | 2015 |
| The (un) reliability of saliency methods PJ Kindermans, S Hooker, J Adebayo, M Alber, KT Schütt, S Dähne, ... Explainable AI: Interpreting, explaining and visualizing deep learning, 267-280, 2019 | 830 | 2019 |
| A benchmark for interpretability methods in deep neural networks S Hooker, D Erhan, PJ Kindermans, B Kim Advances in neural information processing systems 32, 2019 | 806 | 2019 |
Dragomir AnguelovVP and Head of Research, WaymoVerified email at cs.stanford.edu
