Li Yao's Personal Page




I was born and raised in Baotou, China. 
I got my Master's degree in Machine Learning and Data Mining in Aalto University, Finland. 
In 2017, I obtained my PhD from University of Montreal, Canada.
Dissertation: Learning Visual Representations for Image Generation and Video Captioning
Externel examiner: Prof. Sanja Fidler




I'm currently working full-time as a Lead Data Scientist at Enlitic, San Francisco, USA.
My Github
My LinkedIn

Publications


Efficient and Accurate Abnormality Mining from Radiology Reports with Customized False Positive Reduction
Nithya Attaluri, Ahmed Nasir, Carolynne Powe, Harold Racz, Ben Covington, Li Yao, Jordan Prosky, Eric Poblenz, Tobi Olatunji, Kevin Lyman
arXiv preprint 2018 [pdf]

Disease mining from medical reports with high precision!


Weakly Supervised Medical Diagnosis and Localization from Multiple Resolutions
Li Yao, Jordan Prosky, Eric Poblenz, Ben Covington, Kevin Lyman
arXiv preprint 2018 [pdf]

Interpreting diagnostic models with learned saliency maps of high resolution!


Learning to diagnose from scratch by exploiting dependencies among labels
Li Yao, Eric Poblenz, Dmitry Dagunts, Ben Covington, Devon Bernard, Kevin Lyman
arxiv preprint 2017 [pdf]

Deep learning in chest x-ray diagnosis in action!


Delving Deeper into Convolutional Networks for Learning Video Representations 

Nicolas Ballas, Li Yao, Chris Pal, Aaron Courville
International Conference of Learning Representations 2016 [pdf]

We introduced a way to integrate representation multiple layers of ConvNet, without blowing up the number of parameters. New SOTA in video captioning and competitive results on action recognition without using C3D.


Empirical upper bounds for image and video captioning 

Li Yao, Nicolas Ballas, Kyunghyun Cho, John R. Smith, Yoshua Bengio
International Conference of Learning Representations 2016 (workshop) [pdf]

Oracle Performance for Visual Captioning 

Li Yao, Nicolas Ballas, Kyunghyun Cho, John R. Smith, Yoshua Bengio
British Machine Vision Conference (BMVC) 2016 (Oral) [pdf]

In light of recent progress in image and video captioning, this work constructs the trainable, model-based performance upper bounds on different datasets.


Describing Videos by Exploiting Temporal Structure 

Li Yao, Atousa Torabi, Kyunghyun Cho, Nicolas Ballas, Christopher Pal, Hugo Larochelle, Aaron Courville
International Conference of Computer Vision (ICCV15) [pdf] [code]

[poster]

State-of-art results on Youtube2Text on video-to-caption generation, with 3D Conv.Net + LSTM


GSNs : Generative Stochastic Networks 

Guillaume Alain, Yoshua Bengio, Li Yao, Jason Yosinski, Eric Thibodeau-Laufer, Saizheng Zhang, Pascal Vincent
submitted to JMLR [pdf]

A unified framework of GSNs, a new family of generative models in deep learning.


Iterative Neural Autoregressive Distribution Estimator (NADE-k) 

T Raiko, L Yao, K Cho, Y Bengio
Neural Information Processing Systems (NIPS) 2014 [pdf] [code]

How do we push a Deep Orderless NADE further to get the state-of-the-art log-likelihood on both MNIST and Caltech-101.


On the Equivalence Between Deep NADE and Generative Stochastic Networks
L Yao, S Ozair, K Cho, Y Bengio
European Conference on Machine Learning ECML/PKDD 2014 [pdf]

How GSN is mathematically equivalent to a Deep Orderless NADE? We find out. 

Generalized denoising auto-encoders as generative models
Y Bengio, L Yao, G Alain, P Vincent
Neural Information Processing Systems (NIPS) 2013 [pdf] [code]

You think denoising autoencoders are not generative models? Think again:) 

Bounding the test log-likelihood of generative models
Y Bengio, L Yao, G Alain, P Vincent
International Conference on Learning Representations (ICLR) 2013 [pdf]

Generative stochastic networks does not have an analytical solution to the log-likelihood of the data. We show that it is possible to bound it. 


Multimodal Transitions for Generative Stochastic Networks
S Ozair, L Yao, Y Bengio
NIPS Deep learning workshop, 2013 [pdf]

Generative stochastic networks made better by using a NADE model in the reconstruction conditional!. 


Stacked calibration of off-policy policy evaluation for video game matchmaking
E Laufer, RC Ferrari, L Yao, O Delalleau, Y Bengio
IEEE Conference on Computational Intelligence in Games (CIG), 2013 [pdf]

Neural networks in UBISOFT games! See how players are matched together with neural networks.