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<span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:#414142;mso-ansi-language:EN-US" lang="EN-US">Dear Colleagues, </span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></p>
<p class="xxmsonormal" style="margin-right: 0cm; margin-left: 0cm; font-size: 12pt; font-family: "Times New Roman", serif;margin:0cm">
<span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:#414142;mso-ansi-language:EN-US" lang="EN-US">[apologies if you receive many copies of this call<b>]</b></span><span style="font-size:
11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></p>
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<b><span style="font-size:
13.0pt;font-family:"Calibri",sans-serif;color:#414142;mso-ansi-language:EN-US" lang="EN-US"> </span></b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></p>
<p class="xxmsonormal" style="margin-right: 0cm; margin-left: 0cm; font-size: 12pt; font-family: "Times New Roman", serif;margin:0cm;text-align:center" align="center">
<b><span style="font-size:13.0pt;font-family:"Calibri",sans-serif;color:#414142;
mso-ansi-language:EN-US" lang="EN-US">The Third IEEE co-sponsored International Workshop on Deep and Transfer Learning (DTL2020) </span></b><span style="font-size:
11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></p>
<p class="xxmsonormal" style="margin-right: 0cm; margin-left: 0cm; font-size: 12pt; font-family: "Times New Roman", serif;margin:0cm;text-align:center" align="center">
<b><span style="font-size:13.0pt;font-family:"Calibri",sans-serif;color:#414142;
mso-ansi-language:EN-US" lang="EN-US">http://intelligenttech.org/DTL2020/ </span></b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></p>
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<b><span style="font-size:13.0pt;font-family:"Calibri",sans-serif;color:#414142;
mso-ansi-language:EN-US" lang="EN-US">October 19th - 22nd, 2020 – </span></b><b><s><span style="font-size:13.0pt;font-family:"Calibri",sans-serif;color:red;
mso-ansi-language:EN-US" lang="EN-US">Valencia,
Spain</span></s></b><b><span style="font-size:13.0pt;font-family:"Calibri",sans-serif;color:#414142;
mso-ansi-language:EN-US" lang="EN-US"> </span></b><b><span style="font-size:
13.0pt;font-family:"Calibri",sans-serif;color:red;mso-ansi-language:EN-US" lang="EN-US">Online
Presentations!</span></b><b><span style="font-size:13.0pt;
font-family:"Calibri",sans-serif;color:#414142;mso-ansi-language:EN-US" lang="EN-US"> </span></b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></p>
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<span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#414142;
mso-ansi-language:EN-US" lang="EN-US">Co-located with </span><span style="font-size:
11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></p>
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<span style="font-size:11.0pt;font-family:"Calibri",sans-serif;mso-ansi-language:
EN-US" lang="EN-US"><a href="http://intelligenttech.org/IDSTA2020/" target="_blank" data-auth="NotApplicable"><span style="color:#323045">International Conference on Intelligent
Data Science Technologies and Applications (IDSTA2020)</span></a><span style="color:#414142"> </span></span><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif"><o:p> </o:p></span></p>
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background:white" align="center">
<span style="font-size:11.0pt;font-family:"Calibri",sans-serif;
color:red;mso-ansi-language:EN-US" lang="EN-US">All papers accepted to this workshop will be published in IEEE Xplore proceedings of IDSTA. </span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></p>
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<span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#58595B;
mso-ansi-language:EN-US" lang="EN-US"> </span><span style="font-size:11.0pt;font-family:
"Calibri",sans-serif"><o:p> </o:p></span></p>
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<b><span style="background-color:rgba(0,0,0,0)"><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:black;mso-ansi-language:EN-US" lang="EN-US">Call for papers</span><span style="background-color:rgba(0,0,0,0)"> </span></span></b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></p>
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<span style="background-color:rgba(0,0,0,0)"><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:black;mso-ansi-language:EN-US" lang="EN-US">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:</span><span style="background-color:rgba(0,0,0,0)"> </span></span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></p>
<ul style="margin-bottom: 0cm">
<li><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:black;mso-ansi-language:EN-US" lang="EN-US">Deep learning for innovative applications such machine translation, computational biology </span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></li><li><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:black;mso-ansi-language:EN-US" lang="EN-US">Deep Learning for Natural Language Processing </span><span style="font-size:
11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></li><li><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:black;mso-ansi-language:EN-US" lang="EN-US">Deep Learning for Recommender Systems </span><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif"><o:p> </o:p></span></li><li><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:black;mso-ansi-language:EN-US" lang="EN-US">Deep learning for computer vision </span><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif"><o:p> </o:p></span></li><li><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:black;mso-ansi-language:EN-US" lang="EN-US">Deep learning for systems and networks resource management </span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></li><li><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:black;mso-ansi-language:EN-US" lang="EN-US">Optimization for Deep Learning </span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></li><li><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:black;mso-ansi-language:EN-US" lang="EN-US">Deep Reinforcement Learning </span><span style="font-size:11.0pt;font-family:
"Calibri",sans-serif"><o:p> </o:p></span></li><ul style="margin-bottom: 0cm">
<li><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:black;mso-ansi-language:EN-US" lang="EN-US">Deep transfer learning for robots </span><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif"><o:p> </o:p></span></li><li><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:black;mso-ansi-language:EN-US" lang="EN-US">Determining rewards for machines </span><span style="font-size:11.0pt;font-family:
"Calibri",sans-serif"><o:p> </o:p></span></li><li><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:black;mso-ansi-language:EN-US" lang="EN-US">Machine translation </span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></li><li><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:black;mso-ansi-language:EN-US" lang="EN-US">Energy consumption issues in deep reinforcement learning </span><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif"><o:p> </o:p></span></li><li><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:black;mso-ansi-language:EN-US" lang="EN-US">Deep reinforcement learning for game playing </span><span style="font-size:
11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></li><li><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:black;mso-ansi-language:EN-US" lang="EN-US">Stabilize learning dynamics in deep reinforcement learning </span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></li><li><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:black;mso-ansi-language:EN-US" lang="EN-US">Scaling up prior reinforcement learning solutions </span><span style="font-size:
11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></li></ul>
<li><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:black;mso-ansi-language:EN-US" lang="EN-US">Deep Transfer and multi-task learning: </span><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif"><o:p> </o:p></span></li><ul style="margin-bottom: 0cm">
<li><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:black;mso-ansi-language:EN-US" lang="EN-US">New perspectives or theories on transfer and multi-task learning </span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></li><li><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:black;mso-ansi-language:EN-US" lang="EN-US">Dataset bias and concept drift </span><span style="font-size:11.0pt;font-family:
"Calibri",sans-serif"><o:p> </o:p></span></li><li><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:black;mso-ansi-language:EN-US" lang="EN-US">Transfer learning and domain adaptation </span><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif"><o:p> </o:p></span></li><li><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:black;mso-ansi-language:EN-US" lang="EN-US">Multi-task learning </span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></li><li><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:black;mso-ansi-language:EN-US" lang="EN-US">Feature based approaches </span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></li><li><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:black;mso-ansi-language:EN-US" lang="EN-US">Instance based approaches </span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></li><li><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:black;mso-ansi-language:EN-US" lang="EN-US">Deep architectures for transfer and multi-task learning </span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></li><li><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:black;mso-ansi-language:EN-US" lang="EN-US">Transfer across different architectures, e.g. CNN to RNN </span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></li><li><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:black;mso-ansi-language:EN-US" lang="EN-US">Transfer across different modalities, e.g. image to text </span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></li><li><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:black;mso-ansi-language:EN-US" lang="EN-US">Transfer across different tasks, e.g. object recognition and detection </span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></li><li><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:black;mso-ansi-language:EN-US" lang="EN-US">Transfer from weakly labeled or noisy data, e.g. Web data </span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></li></ul>
<li><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:black;mso-ansi-language:EN-US" lang="EN-US">Datasets, benchmarks, and open-source packages </span><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif"><o:p> </o:p></span></li><li><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:black;mso-ansi-language:EN-US" lang="EN-US">Recourse efficient deep learning </span><span style="font-size:11.0pt;font-family:
"Calibri",sans-serif"><o:p> </o:p></span><br>
<span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;mso-ansi-language:EN-US" lang="EN-US"></span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p></o:p></span><br>
<br>
<b><span style="font-size:
11.0pt;font-family:"Calibri",sans-serif;mso-ansi-language:EN-US" lang="EN-US">IMPORTANT DATES </span></b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p>
</o:p></span><br>
<span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;color:red;mso-ansi-language:EN-US" lang="EN-US">Submission Due Date: September 15th, 2020 (Firm Deadline) </span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p>
</o:p></span><br>
<span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;mso-ansi-language:EN-US" lang="EN-US">Notification: September 25th, 2020 </span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p>
</o:p></span><br>
<span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;mso-ansi-language:EN-US" lang="EN-US">Camera-ready submission: October 5th, 2020 </span><span style="font-size:11.0pt;
font-family:"Calibri",sans-serif"><o:p> </o:p></span><br>
<br>
<span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;mso-ansi-language:EN-US" lang="EN-US"> </span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span><br>
<br>
<b><span style="font-size:
11.0pt;font-family:"Calibri",sans-serif;mso-ansi-language:EN-US" lang="EN-US">JOURNAL SPECIAL ISSUES </span></b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p>
</o:p></span><br>
<span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;mso-ansi-language:EN-US" lang="EN-US">Selected papers from the conference will be invited to submit an extended version to the following journal(s). Confirmed Special Issues are: </span><span style="font-size:
11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></li></ul>
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<li class="MsoNormal" style="margin: 0cm 0cm 8pt; line-height: 107%; font-size: 11pt; font-family: "Calibri", sans-serif;mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;
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<span lang="EN-US">Springer Peer-to-Peer Networking and Applications (IF: 2.397) </span><span style="font-size:12.0pt;font-family:
"Times New Roman",serif;mso-bidi-font-family:Arial;mso-bidi-theme-font:
minor-bidi" lang="EN-US"><o:p> </o:p></span></li><li class="MsoNormal" style="margin: 0cm 0cm 8pt; line-height: 107%; font-size: 11pt; font-family: "Calibri", sans-serif;mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;
line-height:normal;mso-list:l1 level1 lfo2;tab-stops:list 36.0pt">
<span lang="EN-US">Springer Cluster Computing (IF: 1.85) <o:p> </o:p></span></li><li class="MsoNormal" style="margin: 0cm 0cm 8pt; line-height: 107%; font-size: 11pt; font-family: "Calibri", sans-serif;mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;
line-height:normal;mso-list:l1 level1 lfo2;tab-stops:list 36.0pt">
<span lang="EN-US">Springer Journal of Network and System Management (IF: 1.676) <o:p> </o:p></span></li><li class="MsoNormal" style="margin: 0cm 0cm 8pt; line-height: 107%; font-size: 11pt; font-family: "Calibri", sans-serif;mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;
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<span lang="EN-US">MDPI Sensors (IF: 3.031) <o:p> </o:p></span></li></ul>
<p class="xxmsonormal" style="margin-right: 0cm; margin-left: 0cm; font-size: 12pt; font-family: "Times New Roman", serif;margin:0cm">
<span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;mso-ansi-language:EN-US" lang="EN-US"> </span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></p>
<p class="xxmsonormal" style="margin-right: 0cm; margin-left: 0cm; font-size: 12pt; font-family: "Times New Roman", serif;margin:0cm">
<span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;mso-ansi-language:EN-US" lang="EN-US">For any inquiries, send us an email at intelligenttechorg@gmail.com. </span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></p>
<p class="xxmsonormal" style="margin-right: 0cm; margin-left: 0cm; font-size: 12pt; font-family: "Times New Roman", serif;margin:0cm">
<span style="font-size:11.0pt;
font-family:"Calibri",sans-serif;mso-ansi-language:EN-US" lang="EN-US"> </span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></p>
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<span lang="EN-US">Best regards,</span><span style="font-size:12.0pt; font-family:"Times New Roman",serif; mso-bidi-font-family:Arial; mso-bidi-theme-font:minor-bidi" lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal" style="margin: 0cm 0cm 8pt; line-height: 107%; font-size: 11pt; font-family: "Calibri", sans-serif">
<span lang="EN-US">IDSTA organising committee<o:p> </o:p></span></p>
<p class="MsoNormal" style="margin: 0cm 0cm 8pt; line-height: 107%; font-size: 11pt; font-family: "Calibri", sans-serif">
<span lang="EN-US"><o:p> </o:p></span></p>
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