| 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 | 67203 | 2015 |
| Batch normalization: Accelerating deep network training by reducing internal covariate shift S Ioffe arXiv preprint arXiv:1502.03167, 2015 | 60567 | 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 | 43920 | 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 | 37607 | 2016 |
| Explaining and harnessing adversarial examples IJ Goodfellow, J Shlens, C Szegedy arXiv preprint arXiv:1412.6572, 2014 | 23653 | 2014 |
| Inception-v4, inception-resnet and the impact of residual connections on learning C Szegedy, S Ioffe, V Vanhoucke, A Alemi Proceedings of the AAAI conference on artificial intelligence 31 (1), 2017 | 18703 | 2017 |
| Intriguing properties of neural networks C Szegedy arXiv preprint arXiv:1312.6199, 2013 | 18612 | 2013 |
| Deeppose: Human pose estimation via deep neural networks A Toshev, C Szegedy Proceedings of the IEEE conference on computer vision and pattern …, 2014 | 4073 | 2014 |
| Deep neural networks for object detection C Szegedy, A Toshev, D Erhan Advances in neural information processing systems 26, 2013 | 2124 | 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 | 1639 | 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 | 1225 | 2014 |
| Scalable, high-quality object detection C Szegedy, S Reed, D Erhan, D Anguelov, S Ioffe arXiv preprint arXiv:1412.1441, 2014 | 572 | 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 29, 2016 | 289* | 2016 |
| Memorizing transformers Y Wu, MN Rabe, DL Hutchins, C Szegedy arXiv preprint arXiv:2203.08913, 2022 | 263 | 2022 |
| Deep network guided proof search S Loos, G Irving, C Szegedy, C Kaliszyk arXiv preprint arXiv:1701.06972, 2017 | 205 | 2017 |
| 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 | 165 | 2019 |
| Object detection using deep neural networks C Szegedy, D Erhan, AT Toshev US Patent 9,275,308, 2016 | 163 | 2016 |
| Autoformalization with large language models Y Wu, AQ Jiang, W Li, M Rabe, C Staats, M Jamnik, C Szegedy Advances in Neural Information Processing Systems 35, 32353-32368, 2022 | 159 | 2022 |
| Graph representations for higher-order logic and theorem proving A Paliwal, S Loos, M Rabe, K Bansal, C Szegedy Proceedings of the AAAI Conference on Artificial Intelligence 34 (03), 2967-2974, 2020 | 139 | 2020 |
| Inception-v4, inception-resnet and the impact of residual connections on learning. CoRR abs/1602.07261 (2016) C Szegedy, S Ioffe, V Vanhoucke arXiv preprint arXiv:1602.07261, 2016 | 109 | 2016 |
Dumitru ErhanDirector of Research @ Google DeepMindBestätigte E-Mail-Adresse bei google.com
