[visionlist] Call for papers for a special issue On “Deep Learning for Information Fusion”

[Mayank Vatsa]

Call for papers for a special issue On “Deep Learning for Information
Fusion”

Information Fusion (Impact Factor: 5.667)

https://www.journals.elsevier.com/information-fusion/call-
for-papers/call-for-papers-for-a-special-issue-on-deep-learning-for-inf

In the last couple of years, deep learning algorithms have pushed the
boundaries for numerous problems in areas such as computer vision, natural
language processing, and audio processing. The performance of advanced
machine (deep) learning algorithms has attained the numbers which were
unexpected a decade ago. For a given problem, information can be obtained
from multiple sources and such multimodal datasets represent information at
varying abstraction levels. Combining information from multiple sources can
further boost the performance. Recent research has also focused on
multimodal deep learning, i.e. representation learning paradigm which
learns joint/combined feature from multiple sources. In this relatively new
area, information from multiple sources are combined in a deep learning
framework. For example, combining audio and video data to obtain joint
feature representation.

This special issue focuses on sharing recent advances in algorithms and
applications that involve combining multiple sources of information using
deep learning. Topics appropriate for this special issue include novel
supervised, unsupervised, semi-supervised and reinforcement algorithms, new
formulations, and applications related to deep learning and information
fusion.

Manuscripts must clearly delineate the role of deep learning information
fusion. The manuscript will be judged solely on the basis of new
contributions excluding the contributions made in earlier publications.
Contributions should be described in sufficient detail to be reproducible
on the basis of the material presented in the paper and the references
cited therein.

Manuscripts should be submitted electronically at:
https://www.evise.com/evise/jrnl/INFFUS

The corresponding author will have to create a user profile if one has not
been established previously at Elsevier.

To ensure that all manuscripts are correctly identified for consideration
in the Special Issue of Deep Learning for Information Fusion, it is
important that authors select “VSI: DL-Fusion".

Deadline for Submission: November 30, 2017



--
Mayank Vatsa, PhD
Vice President (Publications), IEEE Biometrics Council
Head, Infosys Center for Artificial Intelligence
Associate Professor, IIIT-Delhi, India
Adjunct Associate Professor, West Virginia University, USA
http://iab-rubric.org/
http://cai.iiitd.ac.in/
http://ieee-biometrics.org/
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