1.
Silicon Valley AI Lab
Story of end-to-end covfefe
Sanjeev Satheesh
June 2, 2017

2.
Silicon Valley AI Lab
Story of end-to-end models
Sanjeev Satheesh
June 2, 2017

3.
What are End-to-End Models?
Gaussian Mixture
model
over
Spectrograms
Hidden Markov
Model
over
Phonemes
Lexicon +
Language
Model
of
text
CAT

4.
What are End-to-End Models?
English

5.
End-to-end models
Object
Recognition
Speech
Recognition
Image
Captioning
Language
Translation

6.
Why End-to-end models?
Accuracy
Data + Model Size
Deep End-to-End
model
ML workflow-2
ML workflow-1

7.
Traditional machine learning
pipelines are fairly complicated and
typically need a lot of domain knowledge
to build.
Why End-to-end models?

8.
Why End-to-end models?
Easier to obtain a large
amount of data
Easier on practitioners

9.
Why End-to-end models?
Idea
CodeResults

10.
Why End-to-end models?
We built deep speech with no superior knowledge of speech recognition or
Mandarin language

11.
Challenges
Need large amount of
data

12.
Challenges
Need large amount of
data
Lots of compute to
explore architectures

13.
Challenges
Idea
CodeResults

14.
Challenges
Need large amount of
data
Lots of compute to
explore architectures
Lots of compute needed
for deployment.

15.
Batch Dispatch for Efficiency
Time

16.
What’s coming next (immediately)
Speech
Recognition
Speech
Synthesis
Semantic
Understanding
More natural interfaces

17.
What’s coming next (likely)
Composition of E2E models
Super personalization
Tasks we are not solving because
there’s not enough compute

18.
What’s probably NOT coming (immediately)
Autonomous driving
General Dialog systems

19.
Thank You!

20.
Sanjeev Satheesh
sanjeevsatheesh@baidu.com
http://research.baidu.com
Silicon Valley AI Lab