[visionlist] 2nd International Workshop on Big Data Transfer Learning in Conjunction with IEEE BigData Conference 2017, Boston USA

Allan Ding allanzmding at gmail.com
Thu Oct 5 15:06:19 -05 2017


2nd International Workshop on Big Data Transfer Learning (BDTL) in
Conjunction with IEEE BigData Conference 2017
-- Automatic Knowledge Mining and Transfer for Digital Healthcare

**Website**
http://www.cis.umassd.edu/~mshao/BDTL2017/index.html

**Submission**
http://www.cis.umassd.edu/~mshao/BDTL2017/submission.html
<http://www.google.com/url?q=http%3A%2F%2Fwww.cis.umassd.edu%2F~mshao%2FBDTL2017%2Fsubmission.html&sa=D&sntz=1&usg=AFQjCNG1zkk1KED2wc0xlRV0QgZpBjN54A>


**Time**: Dec. 11th, 2017
**Location**: Boston MA, USA

**Important Date**
Oct 10, 2017: Due date for full workshop papers submission
Nov 1, 2017: Notification of paper acceptance to authors
Nov 15, 2017: Camera-ready of accepted papers
Dec 11, 2017: Workshops

**Introduction**
The International Workshop on Big Data Transfer Learning (BDTL) is a serial
workshop ever since 2016. The previous workshop in conjunction with IEEE
BigData 2016 focused on the topic of Big Data Transfer Learning and Text
Mining. This year, the one-day workshop in conjunction with IEEE BigData
2017 will provide a focused international forum to bring together
researchers and research groups to review the status of transfer learning
and knowledge mining, to exploit innovative knowledge transfer methodology
given enormous weakly labeled/multi-source/multi-view/multimodal healthcare
data for disease recognition/prediction, intelligent auxiliary diagnosis
and emerging applications, and to explore future directions particularly in
fields of increasing popularity such as deep learning, smart sensors and
networks, wireless healthcare. The workshop will consist of one to two
invited talks together with peer-reviewed regular papers (oral and poster).
Original high-quality papers are solicited on a wide range of topics
including:

* New perspectives, concepts, or theories on big data transfer learning and
knowledge mining
* Big data transfer learning that works on multimodality, multi-source,
latent domains, or multi-view healthcare data
* Development of analytics tools for emerging and profound digital
healthcare problems
* Comparisons/survey of state-of-the-art analytics tools in health
informatics
* Deep learning, representation learning and convolutional neural networks
for big data analytics in digital healthcare
* Frontier label-free learning methodology for digital healthcare and
health informatics, e.g., one-shot learning, self-taught learning,
generative adversarial networks
* Wireless healthcare, smart sensor networks, wearable devices in big data
analytics and digital healthcare
* New datasets, benchmarks, and open-source software for big data analytics
in digital healthcare

**General Chair**
Y. Raymond Fu, Northeastern University, USA, yu... at ece.neu.edu

**Workshop Co-Chairs**
Yu Cao, UMass Lowell, USA, yc... at cs.uml.edu
Honggang Wang, UMass Dartmouth, USA, hwa... at umassd.edu
Ming Shao, UMass Dartmouth, USA, ms... at umassd.edu

**Web and Publicity Chair**
Zhengming Ding, Northeastern University, USA
Hongfu Liu Northeastern University, USA
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