| Variational autoencoder for deep learning of images, labels and captions Y Pu, Z Gan, R Henao, X Yuan, C Li, A Stevens, L Carin Advances in neural information processing systems 29, 2016 | 982 | 2016 |
| Semantic compositional networks for visual captioning Z Gan, C Gan, X He, Y Pu, K Tran, J Gao, L Carin, L Deng Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 536 | 2017 |
| Alice: Towards understanding adversarial learning for joint distribution matching C Li, H Liu, C Chen, Y Pu, L Chen, R Henao, L Carin Advances in neural information processing systems 30, 2017 | 281 | 2017 |
| l-net: Reconstruct hyperspectral images from a snapshot measurement X Miao, X Yuan, Y Pu, V Athitsos Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 236 | 2019 |
| VAE learning via Stein variational gradient descent Y Pu, Z Gan, R Henao, C Li, S Han, L Carin Advances in Neural Information Processing Systems 30, 2017 | 182* | 2017 |
| Zero-shot learning via class-conditioned deep generative models W Wang, Y Pu, V Verma, K Fan, Y Zhang, C Chen, P Rai, L Carin Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 177 | 2018 |
| Triangle generative adversarial networks Z Gan, L Chen, W Wang, Y Pu, Y Zhang, H Liu, C Li, L Carin Advances in neural information processing systems 30, 2017 | 159 | 2017 |
| Learning generic sentence representations using convolutional neural networks Z Gan, Y Pu, R Henao, C Li, X He, L Carin arXiv preprint arXiv:1611.07897, 2016 | 147* | 2016 |
| Parallel lensless compressive imaging via deep convolutional neural networks X Yuan, Y Pu Optics express 26 (2), 1962-1977, 2018 | 85 | 2018 |
| Symmetric variational autoencoder and connections to adversarial learning L Chen, S Dai, Y Pu, E Zhou, C Li, Q Su, C Chen, L Carin International Conference on Artificial Intelligence and Statistics, 661-669, 2018 | 80 | 2018 |
| Learning weight uncertainty with stochastic gradient mcmc for shape classification C Li, A Stevens, C Chen, Y Pu, Z Gan, L Carin Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 61 | 2016 |
| Continuous-time flows for efficient inference and density estimation C Chen, C Li, L Chen, W Wang, Y Pu, LC Duke International Conference on Machine Learning, 824-833, 2018 | 58 | 2018 |
| A deep generative deconvolutional image model Y Pu, W Yuan, A Stevens, C Li, L Carin Artificial Intelligence and Statistics, 741-750, 2016 | 55 | 2016 |
| Jointgan: Multi-domain joint distribution learning with generative adversarial nets Y Pu, S Dai, Z Gan, W Wang, G Wang, Y Zhang, R Henao, LC Duke International Conference on Machine Learning, 4151-4160, 2018 | 46 | 2018 |
| Scalable bayesian learning of recurrent neural networks for language modeling Z Gan, C Li, C Chen, Y Pu, Q Su, L Carin arXiv preprint arXiv:1611.08034, 2016 | 44 | 2016 |
| Adaptive feature abstraction for translating video to text Y Pu, M Min, Z Gan, L Carin Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 29 | 2018 |
| Tensor-dictionary learning with deep kruskal-factor analysis A Stevens, Y Pu, Y Sun, G Spell, L Carin Artificial Intelligence and Statistics, 121-129, 2017 | 23 | 2017 |
| Image change detection based on the minimum mean square error Y Pu, W Wang, Q Xu 2012 Fifth International Joint Conference on Computational Sciences and …, 2012 | 19 | 2012 |
| Generative deep deconvolutional learning Y Pu, X Yuan, L Carin arXiv preprint arXiv:1412.6039, 2014 | 15* | 2014 |
| Communication-efficient stochastic gradient MCMC for neural networks C Li, C Chen, Y Pu, R Henao, L Carin Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 4173-4180, 2019 | 13 | 2019 |
Chunyuan LiMicrosoft Research, RedmondVerifisert e-postadresse på microsoft.com
