Research of Ying Nian Wu
I am interested in statistical modeling, computing and learning. In particular, I am interested in generative models and unsupervised learning.
SELECTED PAPERS TO DOWNLOAD (list of publications)
R Gao*, J. Xie*, SC Zhu, and YN Wu (2018) Learning grid-like units with vector representation of self-position and matrix representation of self-motion.
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slides
project page
YN Wu, R Gao, T Han, and SC Zhu (2018) A tale of three probabilistic families: discriminative, descriptive and generative Models. Quarterly of Applied Mathematics, accepted. pdf
X Xing, R Gao, T Han, SC Zhu, and YN Wu (2018) Deformable generator network: unsupervised disentanglement of appearance and geometry. ICML workshop on Theoretical Foundations and Applications of Deep Generative Models (TADGM). 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).
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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).
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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.
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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.
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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).
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J Dai, YN Wu, J Zhou, and SC Zhu (2013) Cosegmentation and cosketch by unsupervised learning. Proceedings of International Conference on Computer Vision (ICCV).
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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.
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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.
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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.
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project page
Z Si, H Gong, SC Zhu, YN Wu (2010) Learning active basis models by EM-type algorithms.
Statistical Science, 25, 458-475.
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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.
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YN Wu, Z Si, C Fleming, and SC Zhu (2007) Deformable template as active basis.
Proceedings of International Conference of Computer Vision.
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project page
M Zheng, LO Barrera, B Ren, YN Wu (2007) ChIP-chip: data, model and analysis.
Biometrics, 63, 787-796.
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C Guo, SC Zhu, and YN Wu (2007) Primal sketch: integrating structure
and texture. Computer Vision and Image Understanding, 106, 5-19.
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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.
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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.
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project page
JS Liu, YN Wu (1999) Parameter expansion for data augmentation. Journal of
the American Statistical Association, 94, 1264-1274.
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C Liu, DB Rubin, YN Wu (1998) Parameter expansion to accelerate EM -- the PX-EM
algorithm. Biometrika, 85, 755-770.
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SC Zhu, YN Wu, DB Mumford (1998) Minimax entropy principle and its application
to texture modeling. Neural Computation, 9, 1627-1660.
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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.
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YN Wu (1995) Random shuffling: a new approach to matching problem.
Proceedings of American Statistical Association, 69-74. Longer version
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