Research of Ying Nian Wu

Research interests: representation learning, unsupervised learning, generative models, computer vision, computational neuroscience, bioinformatics.

SELECTED PAPERS TO DOWNLOAD (list of publications)


(learned V1 cells)
R Gao, J Xie, SC Zhu, and YN Wu (2019) Learning vector representation of content and matrix representation of change: towards a representational model of V1. pdf



(learned grid cells)
R Gao*, J Xie*, SC Zhu, and YN Wu (2019) Learning grid cells as vector representation of self-position coupled with matrix representation of self-motion. (* equal contribution). International Conference on Learning Representations. pdf



(faces generated and interpolated by the learned model)
T Han*, E Nijkamp*, X Fang, M Hill, SC Zhu, YN Wu (2019) Divergence triangle for joint training of generator model, energy-based model, and inference model.(* equal contribution). Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pdf


X Xing, R Gao, T Han, SC Zhu, and YN Wu (2019) Unsupervised disentanglement of appearance and geometry by deformable generator network. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pdf



(videos generated by the learned model)
J Xie*, R Gao*, Z Zheng, SC Zhu, and YN Wu (2019) Learning dynamic generator model by alternating back-propagation through time. AAAI-19: 33rd AAAI Conference on Artificial Intelligence. pdf project page


J Xie, Y Lu, R Gao, SC Zhu, and YN Wu (2019) Cooperative learning of descriptor and generator networks. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), accepted. pdf slides project page

YN Wu, R Gao, T Han, and SC Zhu (2019) A tale of three probabilistic families: discriminative, descriptive and generative models. Quarterly of Applied Mathematics, accepted. pdf

T Han, J Wu, and YN Wu (2018) Replicating active appearance model by generator network. International Joint Conference on Artificial Intelligence (IJCAI). pdf

J Xie*, Z Zheng*, R Gao, W Wang, SC Zhu, and YN Wu (2018) Learning descriptor networks for 3D shape synthesis and analysis. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pdf project page (* equal contributions)

R Gao*, Y Lu*, J Zhou, SC Zhu, and YN Wu (2018) Learning generative ConvNets via multigrid modeling and sampling. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pdf project page (* equal contributions)

J Xie, Y Lu, R Gao, and YN Wu (2018) Cooperative learning of energy-based model and latent variable model via MCMC teaching. AAAI-18: 32nd AAAI Conference on Artificial Intelligence. pdf slides project page

J Xie, SC Zhu, and YN Wu (2017) Synthesizing dynamic patterns by spatial-temporal generative ConvNet. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pdf project page

T Han*, Y Lu*, SC Zhu, and YN Wu (2017) Alternating back-propagation for generator network. AAAI-17: 31st AAAI Conference on Artificial Intelligence. pdf project page (* equal contributions)

J Xie*, Y Lu*, SC Zhu, and YN Wu (2016) A theory of generative ConvNet. International Conference on Machine Learning. pdf project page (* equal contributions)

Y Lu, SC Zhu, and YN Wu (2016) Learning FRAME models using CNN filters. AAAI-16: 30th AAAI Conference on Artificial Intelligence. pdf project page

J Xie, Y Lu, SC Zhu, and YN Wu (2016) Inducing wavelets into random fields via generative boosting. Applied and Computational Harmonic Analysis, 41, 4-25. pdf project page

J Dai, Y Lu, and YN Wu (2015) Generative modeling of convolutional neural networks. International Conference on Learning Representations. pdf project page

J Xie, W Hu, SC Zhu, and YN Wu (2014) Learning sparse FRAME models for natural image patterns. International Journal of Computer Vision. pdf project page

J Dai, Y Hong, W Hu, SC Zhu, and YN Wu (2014) Unsupervised learning of dictionaries of hierarchical compositional models. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pdf project page

J Dai, YN Wu, J Zhou, and SC Zhu (2013) Cosegmentation and cosketch by unsupervised learning. Proceedings of International Conference on Computer Vision (ICCV). pdf project page

Y Hong, Z Si, WZ Hu, SC Zhu, and YN Wu (2013) Unsupervised learning of compositional sparse code for natural image representation. Quarterly of Applied Mathematics. pdf project page

YN Wu, Z Si, H Gong, SC Zhu (2010) Learning active basis model for object detection and recognition. International Journal of Computer Vision, 90, 198-235. pdf project page

Si, Z. and Wu, Y. N. (2010) Wavelet, active basis, and shape script --- a tour in the sparse land. ACM SIGMM International Conference on Multimedia Information Retrieval, Special session on Statistical Modeling and Learning for Multimedia. pdf project page

Z Si, H Gong, SC Zhu, YN Wu (2010) Learning active basis models by EM-type algorithms. Statistical Science, 25, 458-475. pdf project page

YN Wu, C Guo, SC Zhu (2008) From information scaling of natural images to regimes of statistical models. Quarterly of Applied Mathematics, 66, 81-122. pdf

YN Wu, Z Si, C Fleming, and SC Zhu (2007) Deformable template as active basis. Proceedings of International Conference of Computer Vision. pdf project page

M Zheng, LO Barrera, B Ren, YN Wu (2007) ChIP-chip: data, model and analysis. Biometrics, 63, 787-796. pdf

C Guo, SC Zhu, and YN Wu (2007) Primal sketch: integrating structure and texture. Computer Vision and Image Understanding, 106, 5-19. pdf project page

C Guo, SC Zhu, and YN Wu (2003) Towards a mathematical theory of primal sketch and sketchability. Proceedings of International Conference of Computer Vision. 1228-1235. pdf project page

G Doretto, A Chiuso, YN Wu, S Soatto (2003) Dynamic textures. International Journal of Computer Vision, 51, 91-109. pdf (source code given in paper) project page

YN Wu, SC Zhu, X Liu (2000) Equivalence of Julesz ensembles and FRAME models. International Journal of Computer Vision, 38, 247-265. pdf project page

JS Liu, YN Wu (1999) Parameter expansion for data augmentation. Journal of the American Statistical Association, 94, 1264-1274. pdf

C Liu, DB Rubin, YN Wu (1998) Parameter expansion to accelerate EM -- the PX-EM algorithm. Biometrika, 85, 755-770. pdf

SC Zhu, YN Wu, DB Mumford (1998) Minimax entropy principle and its application to texture modeling. Neural Computation, 9, 1627-1660. pdf

SC Zhu, YN Wu, DB Mumford (1997) Filter, Random field, And Maximum Entropy (FRAME): towards a unified theory for texture modeling. International Journal of Computer Vision, 27, 107-126. pdf

YN Wu (1995) Random shuffling: a new approach to matching problem. Proceedings of American Statistical Association, 69-74. Longer version pdf