The CityU Online
Seminar named “Big Data, Deep Learning, and Federated Learning Research at
Baidu” was organized by The School of Data Science (SDSC) and The Hong Kong
Institute for Data Science (HKIDS) and was held on 17th Sep
2020. This talk invited Prof. Dejing Dou
(Head of Big Data Lab (BDL) and Business Intelligence Lab (BIL) at Baidu
Research) to share “Open and Inclusive” and Baidu AutoDL which used deep learning
to design and train deep learning models, including Neural Architecture Search,
transfer learning, and interpretability of deep learning.
Prof. Dou said
Big Data and AI would become the “engines” of our times! Then he briefed Big
Data’s 3V that is Volume, Velocity (Speed) and Variety (Complexity).
And then Prof. Dou introduced Big Data Lab at Baidu Research which was founded in collaboration with UN in 2014. Their research areas included Neural Architecture Search (NAS), Transfer Learning, Interpretability of DNN and Federated Learning. He also said AI applications would be industrialized, standardized, modularized and automated.
Because of
current limitations in Deep Learning (DL), Prof. Dou expected the future DL would
be follows:
And then he
briefed three key techniques in AutoDL and they were Self-design Architectures,
Self-adaptive to Tasks and Self-adjusting to Environments.
Baidu AutoDL
included:
1.
Continuing
the research excellence
2.
Cross-modal
Transfer Learning
3.
Continuous
Integration with PaddleHub Finetune API
4.
Continuous
Integration with EasyDL
Prof. Duo also
mentioned Interpretable DL including Explain Models, Explain Training
Procedures and Integration with PaddleX.
He then briefed the milestones of Baidu Auto DL from 1.0 to 3.0.
Finally, Prof.
Duo introduced research in Baidu Business Intelligence Lab (BIL) and its
advance mobile AI technologies including Urban Quantitative Analysis, Urban
Cognitive Computing, Intelligent Online Map Service and Emerging AI Methods &
Applications.
Lastly, he
summarized that Big Data and AI are the core power in this era. Big Data Lab
aimed to transform big data into practice knowledge. He concluded “Open and Inclusive AI” could be
applied in different areas.
Q&A
session performed at the end.
Prof. asked interpretability of DL model into physic
meaning. Prof. Duo said most people focused on prediction results but
explanation of that results (input and output) were also important.
Then I asked
how to verify DL model validity in Small Data. Prof. Duo said to use Transfer Learning
from Big Data model to fit your small data. If it is simple, just use
regression rather than DL.
Reference:
SDSC - https://www.sdsc.cityu.edu.hk/
HKIDS - https://www.cityu.edu.hk/hkids/
Poster - https://www.cityu.edu.hk/sdsc_web/seminar/2020-09-17_sdsc_seminar.pdf
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