| Hierarchical attention networks for document classification Z Yang, D Yang, C Dyer, X He, A Smola, E Hovy Proceedings of the 2016 conference of the North American chapter of the …, 2016 | 1690 | 2016 |
| Stacked attention networks for image question answering Z Yang, X He, J Gao, L Deng, A Smola Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 898 | 2016 |
| Toward controlled generation of text Z Hu, Z Yang, X Liang, R Salakhutdinov, EP Xing arXiv preprint arXiv:1703.00955, 2017 | 355* | 2017 |
| Deep fried convnets Z Yang, M Moczulski, M Denil, N de Freitas, A Smola, L Song, Z Wang Proceedings of the IEEE International Conference on Computer Vision, 1476-1483, 2015 | 205 | 2015 |
| Improved variational autoencoders for text modeling using dilated convolutions Z Yang, Z Hu, R Salakhutdinov, T Berg-Kirkpatrick Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017 | 139 | 2017 |
| Parameter server for distributed machine learning M Li, L Zhou, Z Yang, A Li, F Xia, DG Andersen, A Smola Big Learning NIPS Workshop 6, 2, 2013 | 125 | 2013 |
| A la carte–learning fast kernels Z Yang, A Wilson, A Smola, L Song Artificial Intelligence and Statistics, 1098-1106, 2015 | 102 | 2015 |
| Controllable text generation. arXiv preprint Z Hu, Z Yang, X Liang, R Salakhutdinov, EP Xing arXiv preprint arXiv:1703.00955 7, 2017 | 86 | 2017 |
| On unifying deep generative models Z Hu, Z Yang, R Salakhutdinov, EP Xing arXiv preprint arXiv:1706.00550, 2017 | 71 | 2017 |
| Unsupervised text style transfer using language models as discriminators Z Yang, Z Hu, C Dyer, EP Xing, T Berg-Kirkpatrick Advances in Neural Information Processing Systems, 7287-7298, 2018 | 64 | 2018 |
| Reference-aware language models Z Yang, P Blunsom, C Dyer, W Ling arXiv preprint arXiv:1611.01628, 2016 | 42 | 2016 |
| Neural machine translation with recurrent attention modeling Z Yang, Z Hu, Y Deng, C Dyer, A Smola arXiv preprint arXiv:1607.05108, 2016 | 40 | 2016 |
| Security adoption and influence of cyber-insurance markets in heterogeneous networks Z Yang, JCS Lui Performance Evaluation 74, 1-17, 2014 | 36 | 2014 |
| Deep neural networks with massive learned knowledge Z Hu, Z Yang, R Salakhutdinov, E Xing Proceedings of the 2016 Conference on Empirical Methods in Natural Language …, 2016 | 30 | 2016 |
| Deep generative models with learnable knowledge constraints Z Hu, Z Yang, RR Salakhutdinov, L Qin, X Liang, H Dong, EP Xing Advances in Neural Information Processing Systems, 10501-10512, 2018 | 28 | 2018 |
| The economics of the cloud: price competition and congestion J Anselmi, D Ardagna, JCS Lui, A Wierman, Y Xu, Z Yang ACM SIGMETRICS Performance Evaluation Review 41 (4), 47-49, 2014 | 28 | 2014 |
| The economics of the cloud: price competition and congestion J Anselmi, D Ardagna, JCS Lui, A Wierman, Y Xu, Z Yang ACM SIGMETRICS Performance Evaluation Review 41 (4), 47-49, 2014 | 28 | 2014 |
| A geometry study on the capacity of wireless networks via percolation C Hu, X Wang, Z Yang, J Zhang, Y Xu, X Gao IEEE Transactions on Communications 58 (10), 2916-2925, 2010 | 25 | 2010 |
| Texar: A modularized, versatile, and extensible toolkit for text generation Z Hu, H Shi, B Tan, W Wang, Z Yang, T Zhao, J He, L Qin, D Wang, X Ma, ... arXiv preprint arXiv:1809.00794, 2018 | 16 | 2018 |
| The economics of the cloud J Anselmi, D Ardagna, JCS Lui, A Wierman, Y Xu, Z Yang ACM Transactions on Modeling and Performance Evaluation of Computing Systems …, 2017 | 12 | 2017 |
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