| 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 | 23220 | 2015 |
| Batch normalization: Accelerating deep network training by reducing internal covariate shift S Ioffe, C Szegedy arXiv preprint arXiv:1502.03167, 2015 | 19454 | 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 | 9307 | 2016 |
| Rethinking the inception architecture for computer vision C Szegedy, V Vanhoucke, S Ioffe, J Shlens, Z Wojna Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 8754 | 2016 |
| Inception-v4, inception-resnet and the impact of residual connections on learning C Szegedy, S Ioffe, V Vanhoucke, AA Alemi Thirty-first AAAI conference on artificial intelligence, 2017 | 5034 | 2017 |
| Explaining and harnessing adversarial examples IJ Goodfellow, J Shlens, C Szegedy arXiv preprint arXiv:1412.6572, 2014 | 4970 | 2014 |
| Intriguing properties of neural networks C Szegedy, W Zaremba, I Sutskever, J Bruna, D Erhan, I Goodfellow, ... arXiv preprint arXiv:1312.6199, 2013 | 4820 | 2013 |
| Deeppose: Human pose estimation via deep neural networks A Toshev, C Szegedy Proceedings of the IEEE conference on computer vision and pattern …, 2014 | 1565 | 2014 |
| Deep neural networks for object detection C Szegedy, A Toshev, D Erhan Advances in neural information processing systems, 2553-2561, 2013 | 1025 | 2013 |
| 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 | 861 | 2014 |
| 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 | 375 | 2014 |
| Scalable, high-quality object detection C Szegedy, S Reed, D Erhan, D Anguelov, S Ioffe arXiv preprint arXiv:1412.1441, 2014 | 337 | 2014 |
| Deepmath-deep sequence models for premise selection G Irving, C Szegedy, AA Alemi, N Eén, F Chollet, J Urban Advances in Neural Information Processing Systems, 2235-2243, 2016 | 119 | 2016 |
| Deep network guided proof search S Loos, G Irving, C Szegedy, C Kaliszyk arXiv preprint arXiv:1701.06972, 2017 | 96 | 2017 |
| Modular code generation from synchronous block diagrams: modularity vs. code size R Lublinerman, C Szegedy, S Tripakis Proceedings of the 36th annual ACM SIGPLAN-SIGACT symposium on Principles of …, 2009 | 76 | 2009 |
| Object detection using deep neural networks C Szegedy, D Erhan, AT Toshev US Patent 9,275,308, 2016 | 52 | 2016 |
| Holstep: A machine learning dataset for higher-order logic theorem proving C Kaliszyk, F Chollet, C Szegedy arXiv preprint arXiv:1703.00426, 2017 | 48 | 2017 |
| Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv e-prints S Ioffe, C Szegedy | 36 | 2015 |
| HOList: An environment for machine learning of higher order logic theorem proving K Bansal, S Loos, M Rabe, C Szegedy, S Wilcox International Conference on Machine Learning, 454-463, 2019 | 29* | 2019 |
| Processing images using deep neural networks C Szegedy, VO Vanhoucke US Patent 9,715,642, 2017 | 25 | 2017 |
Dumitru ErhanStaff Research Scientist, Google BrainVerified email at google.com
