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 | 65620 | 2015 |
Batch normalization: Accelerating deep network training by reducing internal covariate shift S Ioffe arXiv preprint arXiv:1502.03167, 2015 | 59228 | 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 | 42199 | 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 | 36368 | 2016 |
Explaining and harnessing adversarial examples IJ Goodfellow, J Shlens, C Szegedy arXiv preprint arXiv:1412.6572, 2014 | 22747 | 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 | 18249 | 2017 |
Intriguing properties of neural networks C Szegedy arXiv preprint arXiv:1312.6199, 2013 | 17998 | 2013 |
Deeppose: Human pose estimation via deep neural networks A Toshev, C Szegedy Proceedings of the IEEE conference on computer vision and pattern …, 2014 | 3955 | 2014 |
Deep neural networks for object detection C Szegedy, A Toshev, D Erhan Advances in neural information processing systems 26, 2013 | 2083 | 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 | 1618 | 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 | 1193 | 2014 |
Scalable, high-quality object detection C Szegedy, S Reed, D Erhan, D Anguelov, S Ioffe arXiv preprint arXiv:1412.1441, 2014 | 564 | 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 | 284* | 2016 |
Memorizing transformers Y Wu, MN Rabe, DL Hutchins, C Szegedy arXiv preprint arXiv:2203.08913, 2022 | 243 | 2022 |
Deep network guided proof search S Loos, G Irving, C Szegedy, C Kaliszyk arXiv preprint arXiv:1701.06972, 2017 | 201 | 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 | 160 | 2019 |
Object detection using deep neural networks C Szegedy, D Erhan, AT Toshev US Patent 9,275,308, 2016 | 153 | 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 | 142 | 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 | 131 | 2020 |
Holstep: A machine learning dataset for higher-order logic theorem proving C Kaliszyk, F Chollet, C Szegedy arXiv preprint arXiv:1703.00426, 2017 | 103 | 2017 |