| 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 | 53773 | 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 | 34071 | 2016 |
| Intriguing properties of neural networks C Szegedy, W Zaremba, I Sutskever, J Bruna, D Erhan, I Goodfellow, ... arXiv preprint arXiv:1312.6199, 2013 | 14342 | 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 | 6870 | 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 | 3356 | 2010 |
| Deep neural networks for object detection C Szegedy, A Toshev, D Erhan Advances in neural information processing systems 26, 1-9, 2013 | 1911 | 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 | 1704 | 2017 |
| 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 | 1647 | 2013 |
| Visualizing higher-layer features of a deep network D Erhan, Y Bengio, A Courville, P Vincent University of Montreal 1341 (3), 1, 2009 | 1528 | 2009 |
| 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 | 1481 | 2014 |
| Domain separation networks K Bousmalis, G Trigeorgis, N Silberman, D Krishnan, D Erhan Advances in neural information processing systems 29, 2016 | 1463 | 2016 |
| 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 | 1338 | 2007 |
| 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 | 1071* | 2016 |
| 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 | 1044 | 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 | 975 | 2016 |
| Model-based reinforcement learning for atari L Kaiser, M Babaeizadeh, P Milos, B Osinski, RH Campbell, ... arXiv preprint arXiv:1903.00374, 2019 | 747 | 2019 |
| The difficulty of training deep architectures and the effect of unsupervised pre-training D Erhan, PA Manzagol, Y Bengio, S Bengio, P Vincent Artificial intelligence and statistics, 153-160, 2009 | 608 | 2009 |
| 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 | 603 | 2019 |
| Zero-data learning of new tasks. H Larochelle, D Erhan, Y Bengio AAAI 1 (2), 3, 2008 | 582 | 2008 |
| Scalable, high-quality object detection C Szegedy, S Reed, D Erhan, D Anguelov, S Ioffe arXiv preprint arXiv:1412.1441, 2014 | 526 | 2014 |
Dragomir AnguelovDistinguished Researcher, Waymo在 cs.stanford.edu 的电子邮件经过验证
