[hpc-announce] IEEE DTL2021: Final Call

Mohammad Alsmirat msmirat at gmail.com
Fri Sep 10 17:14:54 CDT 2021

[Dear Colleagues, apologies if you receive multiple copies of this message]



*http://intelligenttech.org/DTL2021/ <http://intelligenttech.org/DTL2021/>*

Colocated with

The IEEE International Conference on Intelligent Data Science Technologies
and Applications (IDSTA2021)


Deep learning approaches have caused tremendous advances in many areas of
computer science. Deep learning is a branch of machine learning where the
learning process is done using deep and complex architectures such as
recurrent convolutional artificial neural networks. Many computer science
applications have utilized deep learning such as computer vision, speech
recognition, natural language processing, sentiment analysis, social
network analysis, and robotics. The success of deep learning enabled the
application of learning models such as reinforcement learning in which the
learning process is only done by trial-and-error, solely from actions,
rewards or punishments. Deep reinforcement learning comes to create systems
that can learn how to adapt in the real world. As deep learning utilizes
deep and complex architectures, the learning process usually is time and
effort consuming and needs huge labeled data sets. This inspired the
introduction of transfer and multi-task learning approaches to better
exploit the available data during training and adapt previously learned
knowledge to emerging domains, tasks, or applications.

Despite the fact that many research activities are ongoing in these areas,
many challenges are still unsolved. This workshop will bring together
researchers working on deep learning, working on the intersection of deep
learning and reinforcement learning, and/or using transfer learning to
simplify deep learning, and it will help researchers with expertise in one
of these fields to learn about the others. The workshop also aims to bridge
the gap between theories and practices by providing the researchers and
practitioners the opportunity to share ideas and discuss and criticize
current theories and results. We invite the submission of original papers
on all topics related to deep learning, deep reinforcement learning, and
transfer and multi-task learning, with special interest in but not limited


   Deep Learning for Natural Language Processing

   Deep Learning for Recommender Systems

   Deep learning for computer vision

   Deep learning for systems and networks resource management

   Optimization for Deep Learning

   Deep Reinforcement Learning

      Deep transfer learning for robots

      Determining rewards for machines

      Machine translation

      Energy consumption issues in deep reinforcement learning

      Deep reinforcement learning for game playing

      Stabilize learning dynamics in deep reinforcement learning

      Scaling up prior reinforcement learning solutions

   Deep Transfer and multi-task learning:

      New perspectives or theories on transfer and multi-task learning

      Dataset bias and concept drift

      Transfer learning and domain adaptation

      Multi-task learning

      Feature based approaches

      Instance based approaches

      Deep architectures for transfer and multi-task learning

      Transfer across different architectures, e.g. CNN to RNN

      Transfer across different modalities, e.g. image to text

      Transfer across different tasks, e.g. object recognition and detection

      Transfer from weakly labeled or noisy data, e.g. Web data

   Datasets, benchmarks, and open-source packages

   Recourse efficient deep learning



   Full paper submission: September 25th, 2021

   Full paper acceptance notification: October 5th, 2021

   Full paper camera-ready submission: October 20th, 2021

*Submission Site:*


*Paper format*

Submitted papers (.pdf format) must use the A4 IEEE Manuscript Templates
for Conference Proceedings
<https://www.ieee.org/conferences/publishing/templates.html>. Please
remember to add Keywords to your submission.


Submitted papers may be 6 to 8 pages. Up to two additional pages may be
added for references. The reference pages must only contain references.
Overlength papers will be rejected without review.


Papers submitted to DTL must be the original work of the authors. They may
not be simultaneously under review elsewhere. Publications that have been
peer-reviewed and have appeared at other conferences or workshops may not
be submitted to DTL. Authors should be aware that IEEE has a strict policy
with regard to plagiarism
https://www.ieee.org/publications/rights/plagiarism/plagiarism-faq.html The
authors' prior work must be cited appropriately.


All papers that are accepted, registered, and presented in IDSTA2021 and
the workshops co-located with it will be submitted to IEEEXplore for
possible publication.

Best regards,

IDSTA organising committee

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