[agents] The Fourth International Workshop on Deep and Transfer Learning (DTL2024), 24-27 Sept. 2024 | DUBROVNIK, CROATIA.

[Emerging Tech]

[Apologies if you got multiple copies of this invitation]

The Fourth International Workshop on Deep and Transfer Learning (DTL2024)

Hybrid Event

https://iccns-conference.org/2024/Workshops/DTL2024/

24-27 Sept. 2024 | DUBROVNIK, CROATIA.

Technically Co-Sponsored by IEEE Croatia section

*DTL 2024 CFP:*

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 come 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 need 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 is ongoing in these areas,
many challenging 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 leaning, 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
to:

   - Deep learning for innovative applications such machine translation,
   computational biology
   - 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

  *Submissions Guidelines and Proceedings*

Manuscripts should be prepared in 10-point font using the IEEE 8.5" x 11"
two-column format. All papers should be in PDF format, and submitted
electronically at Paper Submission Link. A full paper can be up to 6 pages
(including all figures, tables and references). Submitted papers must
present original unpublished research that is not currently under review
for any other conference or journal. Papers not following these guidelines
may be rejected without review. Also submissions received after the due
date, exceeding length limit, or not appropriately structured may also not
be considered. Authors may contact the Program Chair for further
information or clarification. All submissions are peer-reviewed by at least
three reviewers. Accepted papers will appear in the ICCNS Proceeding, and
be published by the IEEE Computer Society Conference Publishing Services
and be submitted to IEEE Xplore for inclusion.

Submitted papers must include original work, and must not be under
consideration for another conference or journal. Submission of regular
papers up to 8 pages and must follow the IEEE paper format. Please include
up to 7 keywords, complete postal and email address, and fax and phone
numbers of the corresponding author. Authors of accepted papers are
expected to present their work at the conference.

*Important Dates:*

   - *Paper submission deadline: July 25, 2024 *
   - Notification of acceptance: August 25, 2024
   - Camera-ready Submission: September 5, 2024



*Contact:*

Please send any inquiry on ICCNS to: info at iccns-conference.org
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