[Charlie] Yichuan Tang


I have obtained my PhD from the Machine Learning group at the University of Toronto in July 2015.

Advisors: Geoffrey Hinton and Ruslan Salakhutdinov.


Master of Mathematics in Computer Science, University of Waterloo, 2010.

Bachelor of Applied Science in Mechatronics Engineering, University of Waterloo, 2008.


Curriculum Vitae
Research interests: machine learning, vision, and cognitive science
Contact: tang@cs•toronto•edu



Winner of ICML 2013 REPL workshop's facial expression recognition contest: [contest page] [GPU code]
Winner of ICML 2013 REPL workshop's multimodal learning contest: [contest page] [GPU code]





Learning Generative Models Using Visual Attention

Learning Generative Models Using Visual Attention (NIPS 2014 oral)
Yichuan Tang, Nitish Srivastava and Ruslan Salakhutdinov
In Neural Information Processing Systems, Montreal, Quebec, Canada, 2014.
[pdf] [supp] [bibtex]



Learning Stochastic Feedforward Neural Networks

Learning Stochastic Feedforward Neural Networks (NIPS 2013)
Yichuan Tang and Ruslan Salakhutdinov
In Neural Information Processing Systems, Lake Tahoe, Nevada, USA, 2013.
[pdf] [supp] [bibtex]



Deep Learning with Linear Support Vector Machines

Deep Learning with Linear Support Vector Machines
Yichuan Tang
In Workshop on Representational Learning, ICML 2013, Atlanta, USA, 2013.
[pdf] [bibtex] [code]



Tensor Analyzers

Tensor Analyzers (ICML 2013)
Yichuan Tang, Ruslan Salakhutdinov and Geoffrey Hinton
In 30th International Conference on Machine Learning, Atlanta, USA, 2013.
[pdf] [supp] [bibtex] [code]



Deep Mixtures of Factor Analysers

Deep Mixtures of Factor Analysers (ICML 2012)
Yichuan Tang, Ruslan Salakhutdinov and Geoffrey Hinton
In 29th International Conference on Machine Learning, Edinburgh, Scotland, 2012.
[pdf] [poster] [bibtex] [code]



Deep Lambertian Networks

Deep Lambertian Networks (ICML 2012)
Yichuan Tang, Ruslan Salakhutdinov and Geoffrey Hinton
In 29th International Conference on Machine Learning, Edinburgh, Scotland, 2012.
[pdf] [poster] [bibtex]



Robust Boltzmann Machines for Recognition and Denoising

Robust Boltzmann Machines for Recognition and Denoising (CVPR 2012)
Yichuan Tang, Ruslan Salakhutdinov and Geoffrey Hinton
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012, Providence, Rhode Island
[pdf] [poster] [bibtex] [code]



Multiresolution Deep Belief Networks

Multiresolution Deep Belief Networks (AISTATS 2012)
Yichuan Tang and Abdel-rahman Mohamed
Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS), 2012, La Palma, Canary Islands
[pdf] [poster] [bibtex]



Data Normalization in the Learning of RBMs

RBM learning using approximate ML methods is known to be hard when the input data is not sparse. We present an extremely simple data normalization technique to improve learning. The method consists of only 3 additional lines of code and is akin to training on zero-meaned data.


Data Normalization in the Learning of RBMs
Yichuan Tang and Ilya Sutskever
Technical Report, Department of Computer Science, University of Toronto. 2011
[pdf] [poster] [bibtex] [code]



Gated Boltzmann Machine for Recognition under Occlusion

Object recognition in the real world must deal with occlusions and clutter. We investigate a modified version of Deep Boltzmann Machine (DBM) called Denoising Gated Boltzmann Machine (DGBM) which uses feedback dynamics to achieve lower recognition error rates.


Gated Boltzmann Machine for Recognition under Occlusion
Yichuan Tang
In NIPS Workshop on Transfer Learning by Learning Rich Generative Models, Whistler, Canada, 2010.
[pdf] [poster] [bibtex]

DGBM can be used for denoising:
Flash Not Available


Deep Networks for Robust Visual Recognition (ICML 2010)

We show that by performing denoising using a Deep Belief Net prior to classification leads to improved accuracy on noisy and occluded test data.


Deep Networks for Robust Visual Recognition
Yichuan Tang and Chris Eliasmith
In 27th International Conference on Machine Learning, Haifa, Israel, 2010.
[pdf] [poster] [bibtex]





For bugs and/or suggestions, please email me.

Tensor Analyzers Code: TA_code_5.2013.zip

Deep Mixtures of Factor Analyzers Code: DeepMFAcode.zip

Data Normalization in the Learning of RBMs Code: RbmZm_code_10.2012.zip

Robust Boltzmann Machines Code: robm_code_6.15.2012.zip

Gaussian Restricted Boltzmann Machines Code, with sparsity and learning the variances: GaussianRBM.m








Patent:

Tang, Y., Zhou, H. "Method and apparatus for identifying regions of different content in an image", U.S. Patent 7,840,071, Nov. 23, 2010.

Chess engine I wrote as a high school CS project: Here, circa 2003.








I completed my Masters of Mathematics degree in 2010 at the University of Waterloo in Computer Science.
My supervisor was Chris Eliasmith.

Prior to my Masters degree, I was part of the class of 2008 in Mechatronics Engineering at the University of Waterloo. I went to the excellent Thomas Worthington HS and Forest Hill Collegiate Institute.
I'm also a Canadian master at the game of chess and two time ('01 & '02) high school chess champion in the state of Ohio.




Last updated, Oct. 2016