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<p class="MsoNormal" style="line-height:105%;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span>[Apologies if you got multiple copies of this
invitation]<span></span></span></p>
<p class="MsoNormal" align="center" style="text-align:center;line-height:105%;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:15.5pt;line-height:105%">The Fourth International Workshop on Deep and
Transfer Learning (DTL2024)<span></span></span></p>
<p class="MsoNormal" align="center" style="text-align:center;line-height:105%;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:13.5pt;line-height:105%;color:red">Hybrid Event</span><span style="font-size:12pt;line-height:105%"><span></span></span></p>
<p class="MsoNormal" align="center" style="text-align:center;line-height:105%;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:13.5pt;line-height:105%"><a href="https://iccns-conference.org/2024/Workshops/DTL2024/" style="color:blue;text-decoration:underline" target="_blank">https://iccns-conference.org/2024/Workshops/DTL2024/</a><span></span></span></p>
<p class="MsoNormal" align="center" style="text-align:center;line-height:105%;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:13.5pt;line-height:105%;color:black">24-27 Sept. 2024 | DUBROVNIK,
CROATIA.</span><span><span></span></span></p>
<p class="MsoNormal" align="center" style="text-align:center;line-height:105%;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:13.5pt;line-height:105%;color:red">Technically Co-Sponsored by IEEE
Croatia section </span><span><span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;font-size:11pt;font-family:"Calibri",sans-serif"><b><u><span style="font-size:14pt;font-family:"Times New Roman",serif">DTL 2024 CFP:</span></u></b><span><span></span></span></p>
<p class="MsoNormal" style="text-align:justify;line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">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. <span></span></span></p>
<p class="MsoNormal" style="text-align:justify;line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">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: <span></span></span></p>
<ul type="disc" style="margin-bottom:0in"><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">Deep learning for innovative applications such machine
translation, computational biology<span></span></span></li><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">Deep Learning for Natural Language Processing<span></span></span></li><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">Deep Learning for Recommender Systems<span></span></span></li><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">Deep learning for computer vision<span></span></span></li><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">Deep learning for systems and networks resource
management<span></span></span></li><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">Optimization for Deep Learning<span></span></span></li><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">Deep Reinforcement Learning <span></span></span></li><ul type="circle" style="margin-bottom:0in"><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">Deep transfer learning for robots<span></span></span></li><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">Determining rewards for machines<span></span></span></li><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">Machine translation<span></span></span></li><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">Energy consumption issues in deep reinforcement
learning<span></span></span></li><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">Deep reinforcement learning for game playing<span></span></span></li><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">Stabilize learning dynamics in deep reinforcement
learning<span></span></span></li><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">Scaling up prior reinforcement learning solutions<span></span></span></li></ul><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">Deep Transfer and multi-task learning: <span></span></span></li><ul type="circle" style="margin-bottom:0in"><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">New perspectives or theories on transfer and
multi-task learning<span></span></span></li><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">Dataset bias and concept drift<span></span></span></li><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">Transfer learning and domain adaptation<span></span></span></li><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">Multi-task learning<span></span></span></li><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">Feature based approaches<span></span></span></li><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">Instance based approaches<span></span></span></li><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">Deep architectures for transfer and multi-task
learning<span></span></span></li><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">Transfer across different architectures, e.g. CNN to
RNN<span></span></span></li><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">Transfer across different modalities, e.g. image to
text<span></span></span></li><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">Transfer across different tasks, e.g. object
recognition and detection<span></span></span></li><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">Transfer from weakly labeled or noisy data, e.g. Web
data<span></span></span></li></ul><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">Datasets, benchmarks, and open-source packages<span></span></span></li><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">Recourse efficient deep learning<span></span></span></li></ul>
<p class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-family:"Times New Roman",serif"> </span><b><u><span style="font-size:12pt;font-family:"Times New Roman",serif">Submissions Guidelines and Proceedings</span></u></b><span><span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in 12pt;text-align:justify;line-height:105%;font-size:11pt;font-family:"Calibri",sans-serif"><span>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. <br>
<br>
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. <span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in 12pt;text-align:justify;line-height:105%;font-size:11pt;font-family:"Calibri",sans-serif"><u><span style="font-size:13.5pt;line-height:105%;font-family:"Calibri Light",sans-serif;color:black">Important Dates:</span></u><span><span></span></span></p>
<ul style="margin-top:0in;margin-bottom:0in" type="disc"><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><b><span style="font-size:14pt;font-family:"Calibri Light",sans-serif">Paper submission deadline: <span style="color:red">July 25, 2024 </span></span></b><span><span></span></span></li><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:14pt;font-family:"Calibri Light",sans-serif">Notification of acceptance:
August 25, 2024 </span><span><span></span></span></li><li class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:14pt;font-family:"Calibri Light",sans-serif">Camera-ready Submission: September
5, 2024</span><span><span></span></span></li></ul>
<p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:105%;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-family:"Calibri Light",sans-serif"> </span><span><span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:105%;font-size:11pt;font-family:"Calibri",sans-serif"><b><u><span style="font-size:12pt;line-height:105%;font-family:"Calibri Light",sans-serif">Contact:</span></u></b><span><span></span></span></p>
<p class="MsoNormal" style="line-height:105%;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:13.5pt;line-height:105%;font-family:"Calibri Light",sans-serif">Please send any
inquiry on ICCNS to: <a href="mailto:info@iccns-conference.org" style="color:blue;text-decoration:underline" target="_blank">info@iccns-conference.org</a></span><span><span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:"Calibri",sans-serif"><span> </span></p>
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