Chongxuan Li
Chongxuan Li
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Towards better analysis of deep convolutional neural networks
M Liu, J Shi, Z Li, C Li, J Zhu, S Liu
IEEE transactions on visualization and computer graphics 23 (1), 91-100, 2016
Triple generative adversarial nets
LI Chongxuan, T Xu, J Zhu, B Zhang
Advances in neural information processing systems, 4088-4098, 2017
Learning to generate with memory
C Li, J Zhu, B Zhang
International Conference on Machine Learning, 1177-1186, 2016
Max-margin deep generative models
C Li, J Zhu, T Shi, B Zhang
arXiv preprint arXiv:1504.06787, 2015
Learning to write stylized chinese characters by reading a handful of examples
D Sun, T Ren, C Li, H Su, J Zhu
arXiv preprint arXiv:1712.06424, 2017
Max-margin deep generative models for (semi-) supervised learning
C Li, J Zhu, B Zhang
IEEE transactions on pattern analysis and machine intelligence 40 (11), 2762 …, 2017
Graphical generative adversarial networks
C Li, M Welling, J Zhu, B Zhang
arXiv preprint arXiv:1804.03429, 2018
Collaborative filtering with user-item co-autoregressive models
C Du, C Li, Y Zheng, J Zhu, B Zhang
Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018
To relieve your headache of training an mrf, take advil
C Li, C Du, K Xu, M Welling, J Zhu, B Zhang
arXiv preprint arXiv:1901.08400, 2019
Learning implicit generative models by teaching explicit ones
C Du, K Xu, C Li, J Zhu, B Zhang
arXiv preprint arXiv:1807.03870, 2018
Bi-level score matching for learning energy-based latent variable models
F Bao, C Li, K Xu, H Su, J Zhu, B Zhang
arXiv preprint arXiv:2010.07856, 2020
Understanding and Stabilizing GANs' Training Dynamics with Control Theory
K Xu, C Li, J Zhu, B Zhang
arXiv preprint arXiv:1909.13188, 2019
Efficient learning of generative models via finite-difference score matching
T Pang, K Xu, C Li, Y Song, S Ermon, J Zhu
arXiv preprint arXiv:2007.03317, 2020
Multi-objects generation with amortized structural regularization
K Xu, C Li, J Zhu, B Zhang
arXiv preprint arXiv:1906.03923, 2019
Triple generative adversarial networks
C Li, K Xu, J Liu, J Zhu, B Zhang
arXiv preprint arXiv:1912.09784, 2019
Countering noisy labels by learning from auxiliary clean labels
TW Tsai, C Li, J Zhu
arXiv preprint arXiv:1905.13305, 2019
Population matching discrepancy and applications in deep learning
J Chen, C Li, Y Ru, J Zhu
Proceedings of the 31st International Conference on Neural Information …, 2017
MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering
TW Tsai, C Li, J Zhu
arXiv preprint arXiv:2105.01899, 2021
Implicit Normalizing Flows
C Lu, J Chen, C Li, Q Wang, J Zhu
arXiv preprint arXiv:2103.09527, 2021
ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning
L Wang, K Yang, C Li, L Hong, Z Li, J Zhu
arXiv preprint arXiv:2101.00407, 2021
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