Machine learning: recent progress in China and beyond

Machine learning: recent progress in China and beyond National Science Review 5: 20, 2018 GUEST EDITORIAL doi: 10.1093/nsr/nwx132 Advance access publication 6 December 2017 Special Topic: Machine Learning Zhi-Hua Zhou The name ‘machine learning’ was coined in 1959 [ 1], while the processing, and discusses the advantages and challenges of deep learning. most widely quoted formal definition—‘A computer program Causality plays an important role in explanation, prediction, is said to learn from experience E with respect to some class of decision making, etc., and it is desirable to learn causal knowl- tasks T and performance measure P if its performance at tasks edge from data. In the third perspective, Zhang et al. summarize in T, as measured by P, improves with experience E’—was given their recent progress along this direction. in the first textbook about machine learning by T. Mitchell in Zhang and Yang offer an overview of multi-task learning, 1997 [2]. Roughly speaking, machine learning aims to enable which aims to improve the performance of multiple related computers to improve performance by experience. As experi- ence usually appear as data examples, the main focus of ma- learning tasks by leveraging useful information among them. chine learning is actually about the study and construction of Due to the high cost of data-labeling process, in many tasks, learning algorithms that are able to build predictive or descrip- it is desirable to do weakly supervised learning. Zhou provides an tive models from data. With the increasing demand of comput- introduction to this direction. erized data analysis, machine learning becomes more and more The special topic ends with an interview with T. Dietterich, important, and stirs up the current artificial intelligence (AI) the former president of the Association for the Advancement of boom. Artificial Intelligence (AAAI) (the most prestigious association To reflect the state-of-the-art research progress in the field in the field of AI) and the founding president of the International of machine learning in China and beyond, this special section Machine Learning Society, about exciting recent advances and of the National Science Review presents several timely technical technical challenges of machine learning, as well as its big impact reviews and perspectives, along with a research highlight and an on the world. interview. Zhi-Hua Zhou In ‘Learning representations on graphs’, Zhu highlights re- Professor, National Key Laboratory for Novel Software Technology, Nanjing cent effort about learning with network data that are typically University represented as graphs. Guest Editor of Special Topic of NSR In the perspective ‘Model-driven deep learning’, Xu and Sun E-mail: zhouzh@nju.edu.cn present their recent study about trying to design neural network topology with theoretical foundations, and make the network REFERENCES structure explainable and predictable. 1. Samuel AL. IBM J Res Dev 1959; 3: 211–29. In the perspective ‘Deep learning for natural language pro- 2. Mitchell T. Machine Learning. New York, NY: McGraw Hill, 1997. cessing’, Li summarizes the five major tasks in natural language The Author(s) 2017. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd. All rights reserved. For permissions, plea se e-mail: journals.permissions@oup.com Downloaded from https://academic.oup.com/nsr/article-abstract/5/1/20/4705956 by Ed 'DeepDyve' Gillespie user on 16 March 2018 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png National Science Review Oxford University Press

Machine learning: recent progress in China and beyond

Free
1 page

Loading next page...
1 Page
 
/lp/ou_press/machine-learning-recent-progress-in-china-and-beyond-fzlJxW0Ng8
Publisher
Oxford University Press
Copyright
© The Author(s) 2017. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com
ISSN
2095-5138
eISSN
2053-714X
D.O.I.
10.1093/nsr/nwx132
Publisher site
See Article on Publisher Site

Abstract

National Science Review 5: 20, 2018 GUEST EDITORIAL doi: 10.1093/nsr/nwx132 Advance access publication 6 December 2017 Special Topic: Machine Learning Zhi-Hua Zhou The name ‘machine learning’ was coined in 1959 [ 1], while the processing, and discusses the advantages and challenges of deep learning. most widely quoted formal definition—‘A computer program Causality plays an important role in explanation, prediction, is said to learn from experience E with respect to some class of decision making, etc., and it is desirable to learn causal knowl- tasks T and performance measure P if its performance at tasks edge from data. In the third perspective, Zhang et al. summarize in T, as measured by P, improves with experience E’—was given their recent progress along this direction. in the first textbook about machine learning by T. Mitchell in Zhang and Yang offer an overview of multi-task learning, 1997 [2]. Roughly speaking, machine learning aims to enable which aims to improve the performance of multiple related computers to improve performance by experience. As experi- ence usually appear as data examples, the main focus of ma- learning tasks by leveraging useful information among them. chine learning is actually about the study and construction of Due to the high cost of data-labeling process, in many tasks, learning algorithms that are able to build predictive or descrip- it is desirable to do weakly supervised learning. Zhou provides an tive models from data. With the increasing demand of comput- introduction to this direction. erized data analysis, machine learning becomes more and more The special topic ends with an interview with T. Dietterich, important, and stirs up the current artificial intelligence (AI) the former president of the Association for the Advancement of boom. Artificial Intelligence (AAAI) (the most prestigious association To reflect the state-of-the-art research progress in the field in the field of AI) and the founding president of the International of machine learning in China and beyond, this special section Machine Learning Society, about exciting recent advances and of the National Science Review presents several timely technical technical challenges of machine learning, as well as its big impact reviews and perspectives, along with a research highlight and an on the world. interview. Zhi-Hua Zhou In ‘Learning representations on graphs’, Zhu highlights re- Professor, National Key Laboratory for Novel Software Technology, Nanjing cent effort about learning with network data that are typically University represented as graphs. Guest Editor of Special Topic of NSR In the perspective ‘Model-driven deep learning’, Xu and Sun E-mail: zhouzh@nju.edu.cn present their recent study about trying to design neural network topology with theoretical foundations, and make the network REFERENCES structure explainable and predictable. 1. Samuel AL. IBM J Res Dev 1959; 3: 211–29. In the perspective ‘Deep learning for natural language pro- 2. Mitchell T. Machine Learning. New York, NY: McGraw Hill, 1997. cessing’, Li summarizes the five major tasks in natural language The Author(s) 2017. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd. All rights reserved. For permissions, plea se e-mail: journals.permissions@oup.com Downloaded from https://academic.oup.com/nsr/article-abstract/5/1/20/4705956 by Ed 'DeepDyve' Gillespie user on 16 March 2018

Journal

National Science ReviewOxford University Press

Published: Jan 1, 2018

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off