| 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 | 27199 | 2015 |
| Tensorflow: A system for large-scale machine learning M Abadi, P Barham, J Chen, Z Chen, A Davis, J Dean, M Devin, ... 12th {USENIX} symposium on operating systems design and implementation …, 2016 | 17107* | 2016 |
| 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 | 14441 | 2014 |
| TensorFlow: Large-scale machine learning on heterogeneous systems M Abadi, A Agarwal, P Barham, E Brevdo, Z Chen, C Citro, GS Corrado, ... | 8015 | 2015 |
| 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 | 4124 | 2014 |
| Accurate, large minibatch sgd: Training imagenet in 1 hour P Goyal, P Dollár, R Girshick, P Noordhuis, L Wesolowski, A Kyrola, ... arXiv preprint arXiv:1706.02677, 2017 | 1431 | 2017 |
| 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 | 613 | 2015 |
| 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, 141-165, 2013 | 424 | 2013 |
| Very deep convolutional networks for large-scale image recognition S Karen, A Zisserman arXiv preprint arXiv:1409.1556 1 (2), 2014 | 389 | 2014 |
| 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 Conference on Computer Vision and Pattern …, 2019 | 379 | 2019 |
| Deep convolutional ranking for multilabel image annotation Y Gong, Y Jia, T Leung, A Toshev, S Ioffe arXiv preprint arXiv:1312.4894, 2013 | 369 | 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, ... European conference on computer vision, 48-64, 2014 | 366 | 2014 |
| SNAS: stochastic neural architecture search S Xie, H Zheng, C Liu, L Lin arXiv preprint arXiv:1812.09926, 2018 | 344 | 2018 |
| 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 | 324 | 2012 |
| 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 | 324 | 2008 |
| 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 | 281 | 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 | 257 | 2018 |
| Trace ratio problem revisited Y Jia, F Nie, C Zhang IEEE Transactions on Neural Networks 20 (4), 729-735, 2009 | 245 | 2009 |
| Learning cross-modality similarity for multinomial data Y Jia, M Salzmann, T Darrell 2011 International Conference on Computer Vision, 2407-2414, 2011 | 193 | 2011 |
| Tensorflow: Large-scale machine learning on heterogeneous distributed systems. CoRR abs/1603.04467 (2016) M Abadi, A Agarwal, P Barham, E Brevdo, Z Chen, C Citro, GS Corrado, ... arXiv preprint arXiv:1603.04467, 2016 | 185 | 2016 |
Trevor DarrellProfessor of Computer Science, U.C. Berkeley確認したメール アドレス: eecs.berkeley.edu
