| 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, 2352-2360, 2016 | 443 | 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 | 269 | 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, 5495-5503, 2017 | 160 | 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 | 104* | 2016 |
| 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 | 98 | 2018 |
| 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, 4236-4245, 2017 | 96* | 2017 |
| 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, 5247-5256, 2017 | 96 | 2017 |
| 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 | 54 | 2018 |
| A deep generative deconvolutional image model Y Pu, W Yuan, A Stevens, C Li, L Carin Artificial Intelligence and Statistics, 741-750, 2016 | 48 | 2016 |
| Parallel lensless compressive imaging via deep convolutional neural networks X Yuan, Y Pu Optics express 26 (2), 1962-1977, 2018 | 42 | 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 | 33 | 2016 |
| lambda-Net: Reconstruct Hyperspectral Images From a Snapshot Measurement X Miao, X Yuan, Y Pu, V Athitsos 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 4058-4068, 2019 | 32 | 2019 |
| 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 | 31 | 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 | 26 | 2018 |
| Jointgan: Multi-domain joint distribution learning with generative adversarial nets Y Pu, S Dai, Z Gan, W Wang, G Wang, Y Zhang, R Henao, L Carin arXiv preprint arXiv:1806.02978, 2018 | 25 | 2018 |
| 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 | 23 | 2018 |
| Implicit causal models for genome-wide association studies D Tran, DM Blei arXiv preprint arXiv:1710.10742, 2017 | 23 | 2017 |
| Generative deep deconvolutional learning Y Pu, X Yuan, L Carin arXiv preprint arXiv:1412.6039, 2014 | 19* | 2014 |
| 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 | 15 | 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 | 12 | 2012 |
Lawrence CarinProfessor of Electrical and Computer Engineering, Duke UniversityBestätigte E-Mail-Adresse bei ece.duke.edu
