1. How Microsoft Had Made Deep Learning Red-Hot in IT Industry
Zhijie Yan, Microsoft Research Asia
USTC visit, May 6, 2014

2. Self Introduction @MSRA鄢志杰 Research interests
996 – studied in USTC from 1999 to 2008
Graduate student – studied in iFlytek speech lab from to 2008, supervised by Prof. Renhua Wang
Intern – worked in MSR Asia from 2005 to 2006
Visiting scholar – visited Georgia Tech in 2007
FTE – worked in MSR Asia since 2008
Research interests
Speech, deep learning, large-scale machine learning

3. In Today’s Talk Deep learning becomes very hot in the past few years
How Microsoft had made deep learning hot in IT industry
Deep learning basics
Why Microsoft can turn all these ideas into reality
Further reading materials

4. How Hot is Deep Learning
“This announcement comes on the heels of a $600,000 gift Google awarded Professor Hinton’s research group to support further work in the area of neural nets.” – U. of T. website

5. How Hot is Deep Learning

6. How Hot is Deep Learning

7. How Hot is Deep Learning

8. How Hot is Deep Learning

9. Microsoft Had Made Deep Learning Hot in IT Industry
Initial attempts made by University of Toronto had shown promising results using DL in speech recognition on TIMIT phone recognition task
Prof. Hinton’s student visited MSR as an intern, good results were obtained on Microsoft Bing voice search task
MSR Asia and Redmond collaborated and got amazing results on Switchboard task, which shocked the whole industry

10. Microsoft Had Made Deep Learning Hot in IT Industry
*figure borrowed from MSR principal researcher Li DENG

11. Microsoft Had Made Deep Learning Hot in IT Industry
Followed by others and results were confirmed in various different speech recognition tasks
Google / IBM / Apple / Nuance / 百度 / 讯飞
Continuously advanced by MSR and others
Expand to solve more and more problems
Image processing
Natural language processing
Search …

12. Deep Learning From Speech to Image
ILSVRC-2012 competition on ImageNet
Classification task: classify an image into 1 of the 1,000 classes in your 5 bets
lifeboat
airliner
school bus
Institution
Error rate (%)
University of Amsterdam
29.6
XRCE/INRIA
27.1
Oxford
27.0
ISI
26.2

13. Deep Learning From Speech to Image
ILSVRC-2012 competition on ImageNet
Classification task: classify an image into 1 of the 1,000 classes in your 5 bets
lifeboat
airliner
school bus
Institution
Error rate (%)
University of Amsterdam
29.6
XRCE/INRIA
27.1
Oxford
27.0
ISI
26.2
SuperVision
16.4

14. Deep Learning Basics
Deep learning  deep neural networks  multi-layer perceptron (MLP) with a deep structure (many hidden layers)
Output layer W3
Output layer
Hidden layer W1
Hidden layer W2
Hidden layer W0
Input layer W1
Hidden layer W0
Input layer

15. Deep Learning Basics
Sounds not new at all? Sounds familiar like you’ve learned in class?
Things not change over the years
Network topology / activation functions / …
Backpropagation (BP)
Things changed recently
Data  Big data
General-purpose computing on graphics processing units (GPGPU)
“A bag of tricks” accumulated over the years

16. E.g. Deep Neural Network for Speech Recognition
Three key components that make DNN-HMM work
Tied tri-phones as the
basis units for HMM states
Many layers of nonlinear feature transformation
Long window of frames
*figure borrowed from MSR senior researcher Dong YU

17. E.g. Deep Neural Network for Image Classification
The ILSVRC-2012 winning solution
*figure copied from Krizhevsky, et al., “ImageNet Classification with Deep Convolutional Neural Networks”

18. Scale Out Deep Leaning Training speed was a major problem of DL
Speech recognition model trained with 1,800-hour data (~650,000,000 vector frames) costs 2 weeks using 1 GPU
Image classification model trained with ~1,000,000 figures costs 1 weeks using 2 GPUs*
How to scale out if 10x, 100x training data becomes available?
*Krizhevsky, et al., “ImageNet Classification with Deep Convolutional Neural Networks”

19. DNN-GMM-HMM
Joint work with USTC-MSRA Ph.D. program student, Jian XU (许健, 0510)
The “DNN-GMM-HMM” approach for speech recognition*
DNN as hierarchical nonlinear feature extractor, trained using a sub-set of training data
GMM-HMM as acoustic model, trained using full data
*Z.-J. Yan, Q. Huo, and J. Xu, “A scalable approach to using DNN-derived features in GMM-HMM based acoustic modeling for LVCSR”

20. CMLLR unsupervised adaptation
DNN-GMM-HMM
DNN-derived features
PCA
HLDA
Tied-state WE-RDLT
MMI sequence training
CMLLR unsupervised adaptation
GMM-HMM modeling of DNN-derived features: combine the best of both worlds

21. Experimental Results
300hr DNN (18k states, 7 hidden layers) + 2,000hr GMM-HMM (18k states)*
Training time reduced from 2 weeks to 3-5 days
*Z.-J. Yan, Q. Huo, and J. Xu, “A scalable approach to using DNN-derived features in GMM-HMM based acoustic modeling for LVCSR”

22. A New Optimization Method
Joint work with USTC-MSRA Ph.D. program student, Kai Chen (陈凯, 0700)
Using 20 GPUs, time needed to train a 1,800-hour acoustic model is cut from 2 weeks to 12 hours, without accuracy loss
The magic is to be published
We believe the scalability issue in DNN training for speech recognition is now solved!

23. Why Microsoft Can Do All These Good Things
Research
Bridge the gap between academia and industry via our intern and visiting scholar programs
Scale out from toy problems to real-world industry-scale applications
Product team
Solve practical issues and deploy technologies to serve users worldwide via our services
All together
We continuously improve our work towards larger scale, higher accuracy, and to tackle more challenging tasks
Finally
We have big-data + world-leading computational infrastructure

24. If You Want to Know More About Deep Learning
Neural networks for machine learning:
Prof. Hinton’s homepage:
DeepLearning.net:
Open-source
Kaldi (speech):
cuda-convent (image):

25. Thanks!