View source for VSI: DL-Fusion 2018 : Special issue On “Deep Learning for Information Fusion” - Information Fusion (Elsevier)

{{Event 
|Acronym=VSI: DL-Fusion 2018 
|Title=VSI: DL-Fusion  2018 : Special issue On “Deep Learning for Information Fusion” - Information Fusion (Elsevier) 
|Type=Workshop 
|Field=machine learning
|Start date=Invalid date
|End date=2017/07/28 
|City=N/A 
|Country=undefined
|Homepage=https://www.journals.elsevier.com/information-fusion/call-for-papers/call-for-papers-for-a-special-issue-on-deep-learning-for-inf
|Submission deadline=2017/11/30
}}
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