| 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 | 30261 | 2016 |
| Tensorflow: Large-scale machine learning on heterogeneous distributed systems M Abadi, A Agarwal, P Barham, E Brevdo, Z Chen, C Citro, GS Corrado, ... arXiv preprint arXiv:1603.04467, 2016 | 28438 | 2016 |
| Explaining and harnessing adversarial examples IJ Goodfellow, J Shlens, C Szegedy International Conference on Learning Representations (ICLR), 2014 | 18207 | 2014 |
| Learning transferable architectures for scalable image recognition B Zoph, V Vasudevan, J Shlens, QV Le Proceedings of the IEEE conference on Computer Vision and Pattern …, 2018 | 6200 | 2018 |
| A tutorial on principal component analysis J Shlens arXiv preprint arXiv:1404.1100, 2014 | 3888 | 2014 |
| Conditional image synthesis with auxiliary classifier gans A Odena, C Olah, J Shlens International Conference on Machine Learning (ICML), 2642-2651, 2017 | 3633 | 2017 |
| Devise: A deep visual-semantic embedding model A Frome, G Corrado, J Shlens, S Bengio, J Dean, MA Ranzato, T Mikolov Neural Information Processing Systems, 2013 | 3020 | 2013 |
| Adversarial autoencoders A Makhzani, J Shlens, N Jaitly, I Goodfellow, B Frey arXiv preprint arXiv:1511.05644, 2015 | 2995 | 2015 |
| Randaugment: Practical automated data augmentation with a reduced search space ED Cubuk, B Zoph, J Shlens, QV Le Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 2637 | 2020 |
| Progressive neural architecture search C Liu, B Zoph, M Neumann, J Shlens, W Hua, LJ Li, L Fei-Fei, A Yuille, ... Proceedings of the European conference on computer vision (ECCV), 19-34, 2018 | 2134 | 2018 |
| Scalability in perception for autonomous driving: Waymo open dataset P Sun, H Kretzschmar, X Dotiwalla, A Chouard, V Patnaik, P Tsui, J Guo, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 1917 | 2020 |
| Spatio-temporal correlations and visual signalling in a complete neuronal population JW Pillow, J Shlens, L Paninski, A Sher, AM Litke, EJ Chichilnisky, ... Nature 454 (7207), 995-999, 2008 | 1556 | 2008 |
| Do better imagenet models transfer better? S Kornblith, J Shlens, QV Le Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 1241 | 2019 |
| A learned representation for artistic style V Dumoulin, J Shlens, M Kudlur International Conference on Learning Representations (ICLR), 2016 | 1178 | 2016 |
| Attention augmented convolutional networks I Bello, B Zoph, A Vaswani, J Shlens, QV Le Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 1102 | 2019 |
| Stand-alone self-attention in vision models P Ramachandran, N Parmar, A Vaswani, I Bello, A Levskaya, J Shlens Advances in neural information processing systems 32, 2019 | 1062 | 2019 |
| Zero-shot learning by convex combination of semantic embeddings M Norouzi, T Mikolov, S Bengio, Y Singer, J Shlens, A Frome, GS Corrado, ... International Conference on Learning Representations (ICLR), 2013 | 1030 | 2013 |
| Bottleneck transformers for visual recognition A Srinivas, TY Lin, N Parmar, J Shlens, P Abbeel, A Vaswani Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 847 | 2021 |
| TensorFlow: Large-scale machine learning on heterogeneous systems, software available from tensorflow. org (2015) M Abadi, A Agarwal, P Barham, E Brevdo, Z Chen, C Citro, GS Corrado, ... URL https://www. tensorflow. org, 2015 | 729 | 2015 |
| Net2net: Accelerating learning via knowledge transfer T Chen, I Goodfellow, J Shlens International Conference on Learning Representations (ICLR), 2015 | 668 | 2015 |
Quoc V. LeResearch Scientist, GoogleVerified email at stanford.edu
