| 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 | 71046 | 2015 |
| Ssd: Single shot multibox detector W Liu, D Anguelov, D Erhan, C Szegedy, S Reed, CY Fu, AC Berg European conference on computer vision, 21-37, 2016 | 52403 | 2016 |
| Intriguing properties of neural networks C Szegedy, W Zaremba, I Sutskever, J Bruna, D Erhan, I Goodfellow, ... arXiv preprint arXiv:1312.6199, 2013 | 21289 | 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 | 8863 | 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 | 3890 | 2010 |
| Challenges in representation learning: A report on three machine learning contests IJ Goodfellow, D Erhan, PL Carrier, A Courville, M Mirza, B Hamner, ... International conference on neural information processing, 117-124, 2013 | 2702 | 2013 |
| Deep neural networks for object detection C Szegedy, A Toshev, D Erhan Advances in neural information processing systems 26, 1-9, 2013 | 2287 | 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 | 2118 | 2017 |
| Visualizing higher-layer features of a deep network D Erhan, Y Bengio, A Courville, P Vincent University of Montreal 1341 (3), 1, 2009 | 1997 | 2009 |
| Domain separation networks K Bousmalis, G Trigeorgis, N Silberman, D Krishnan, D Erhan Advances in neural information processing systems 29, 2016 | 1992 | 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 | 1693 | 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 | 1512 | 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 | 1347 | 2014 |
| Model-based reinforcement learning for atari L Kaiser, M Babaeizadeh, P Milos, B Osinski, RH Campbell, ... arXiv preprint arXiv:1903.00374, 2019 | 1271 | 2019 |
| 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 | 1228 | 2016 |
| 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 | 1225 | 2015 |
| Theano: A Python framework for fast computation of mathematical expressions R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ... arXiv preprint arXiv:1605.02688, 2016 | 1181 | 2016 |
| 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 | 1021 | 2019 |
| 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 | 977 | 2019 |
| Zero-data learning of new tasks. H Larochelle, D Erhan, Y Bengio AAAI 1 (2), 3, 2008 | 797 | 2008 |
Dragomir AnguelovVP and Head of Research, WaymoVerified email at cs.stanford.edu
