| 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 | 64723 | 2015 |
| TensorFlow: Large-scale machine learning on heterogeneous systems M Abadi, A Agarwal, P Barham, E Brevdo, Z Chen, C Citro, GS Corrado, ... | 32808* | 2015 |
| Caffe: Convolutional architecture for fast feature embedding Y Jia, E Shelhamer, J Donahue, S Karayev, J Long, R Girshick, ... Proceedings of the 22nd ACM international conference on Multimedia, 675-678, 2014 | 18691 | 2014 |
| Decaf: A deep convolutional activation feature for generic visual recognition J Donahue, Y Jia, O Vinyals, J Hoffman, N Zhang, E Tzeng, T Darrell International conference on machine learning, 647-655, 2014 | 6191 | 2014 |
| Accurate, large minibatch SG D: training imagenet in 1 hour P Goyal arXiv preprint arXiv:1706.02677, 2017 | 4274 | 2017 |
| Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search B Wu, X Dai, P Zhang, Y Wang, F Sun, Y Wu, Y Tian, P Vajda, Y Jia, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 1620 | 2019 |
| Matchnet: Unifying feature and metric learning for patch-based matching X Han, T Leung, Y Jia, R Sukthankar, AC Berg Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 1080 | 2015 |
| Proceedings of the IEEE conference on computer vision and pattern recognition C Szegedy, W Liu, Y Jia, P Sermanet, S Reed, D Anguelov, D Erhan, ... Going deeper with convolutions, 1-9, 2015 | 985 | 2015 |
| Applied machine learning at facebook: A datacenter infrastructure perspective K Hazelwood, S Bird, D Brooks, S Chintala, U Diril, D Dzhulgakov, ... 2018 IEEE international symposium on high performance computer architecture …, 2018 | 780 | 2018 |
| A category-level 3d object dataset: Putting the kinect to work A Janoch, S Karayev, Y Jia, JT Barron, M Fritz, K Saenko, T Darrell Consumer depth cameras for computer vision: research topics and applications …, 2013 | 579 | 2013 |
| Machine learning at facebook: Understanding inference at the edge CJ Wu, D Brooks, K Chen, D Chen, S Choudhury, M Dukhan, ... 2019 IEEE international symposium on high performance computer architecture …, 2019 | 577 | 2019 |
| Deep convolutional ranking for multilabel image annotation Y Gong, Y Jia, T Leung, A Toshev, S Ioffe arXiv preprint arXiv:1312.4894, 2013 | 574 | 2013 |
| Large-scale object classification using label relation graphs J Deng, N Ding, Y Jia, A Frome, K Murphy, S Bengio, Y Li, H Neven, ... Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland …, 2014 | 565 | 2014 |
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| Trace ratio criterion for feature selection F Nie, S Xiang, Y Jia, C Zhang, S Yan Proceedings of the 23rd national conference on Artificial intelligence 2 …, 2008 | 470 | 2008 |
| Beyond spatial pyramids: Receptive field learning for pooled image features Y Jia, C Huang, T Darrell 2012 IEEE Conference on Computer Vision and Pattern Recognition, 3370-3377, 2012 | 388 | 2012 |
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| Chamnet: Towards efficient network design through platform-aware model adaptation X Dai, P Zhang, B Wu, H Yin, F Sun, Y Wang, M Dukhan, Y Hu, Y Wu, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 333 | 2019 |
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Pierre SermanetResearch Scientist, GoogleVerified email at google.com
