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<font color="#323045"><font face="Arial, serif"><font style="font-size:12pt" size="3"><span style="font-size: 10pt; line-height: normal;">Dear Colleagues,</span><br>
<span style="font-size: 10pt; line-height: normal;">[Apologies if you receive multiple copies of this message]</span></font></font></font></p>
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<font color="#000080"><span lang="zxx"><span style="text-transform:uppercase"><font color="#323045"><font face="Arial, serif"><font style="font-size:16pt" size="4"><span style="letter-spacing:0.3pt"><span lang="en-AE"><span>The Fourth International Workshop
on Deep and Transfer Learning (DTL2021)</span></span></span></font></font></font></span></span></font></p>
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<font style="font-size:14pt" size="4"><span style="text-transform:uppercase"><font color="#414142"><font face="Arial, serif"><span style="letter-spacing: 0.3pt; font-size: 12pt; line-height: normal;">NOVEMBER 15-17, 2021, TARTU, ESTONIA</span></font></font></span></font></p>
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<font color="#000080"><span lang="zxx"><u><font color="#58595b"><font style="font-size:14pt" size="4"><span style="font-size: 12pt; line-height: normal;" lang="en-AE">http://intelligenttech.org/DTL2021/</span></font></font></u></span></font></p>
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<font color="#323045"><font face="Arial, serif"><font style="font-size:14pt" size="4"><span style="font-size: 12pt; line-height: normal;">Colocated with</span></font></font></font></p>
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<font color="#323045"><font face="Arial, serif"><font style="font-size:14pt" size="4"><span style="font-size: 12pt; line-height: normal;">The Second International Conference on Intelligent Data Science Technologies and Applications (IDSTA2021)</span></font></font></font></p>
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<b><font color="#58595b"><font style="font-size:12pt" size="3">CALL FOR PAPERS</font></font></b></p>
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<font style="font-size:12pt" size="3"><font color="#58595b">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.
</font></font></p>
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<font color="#58595b"><font style="font-size:12pt" size="3">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:
</font></font></p>
<ul>
<li>
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<font color="#58595b">Deep Learning for Natural Language Processing </font></p>
</li><li>
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<font color="#58595b">Deep Learning for Recommender Systems </font></p>
</li><li>
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<font color="#58595b">Deep learning for computer vision </font></p>
</li><li>
<p class="western" style="margin-bottom:0cm;line-height:115%;text-align:left;background:transparent none repeat scroll 0% 0%;font-family:"Calibri", serif;font-size:11pt">
<font color="#58595b">Deep learning for systems and networks resource management </font>
</p>
</li><li>
<p class="western" style="margin-bottom:0cm;line-height:115%;text-align:left;background:transparent none repeat scroll 0% 0%;font-family:"Calibri", serif;font-size:11pt">
<font color="#58595b">Optimization for Deep Learning </font></p>
</li><li>
<p class="western" style="margin-bottom:0cm;line-height:115%;text-align:left;background:transparent none repeat scroll 0% 0%;font-family:"Calibri", serif;font-size:11pt">
<font color="#58595b">Deep Reinforcement Learning </font></p>
<ul>
<li>
<p class="western" style="margin-bottom:0cm;line-height:115%;text-align:left;background:transparent none repeat scroll 0% 0%;font-family:"Calibri", serif;font-size:11pt">
<font color="#58595b">Deep transfer learning for robots </font></p>
</li><li>
<p class="western" style="margin-bottom:0cm;line-height:115%;text-align:left;background:transparent none repeat scroll 0% 0%;font-family:"Calibri", serif;font-size:11pt">
<font color="#58595b">Determining rewards for machines </font></p>
</li><li>
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<font color="#58595b">Machine translation </font></p>
</li><li>
<p class="western" style="margin-bottom:0cm;line-height:115%;text-align:left;background:transparent none repeat scroll 0% 0%;font-family:"Calibri", serif;font-size:11pt">
<font color="#58595b">Energy consumption issues in deep reinforcement learning </font>
</p>
</li><li>
<p class="western" style="margin-bottom:0cm;line-height:115%;text-align:left;background:transparent none repeat scroll 0% 0%;font-family:"Calibri", serif;font-size:11pt">
<font color="#58595b">Deep reinforcement learning for game playing </font></p>
</li><li>
<p class="western" style="margin-bottom:0cm;line-height:115%;text-align:left;background:transparent none repeat scroll 0% 0%;font-family:"Calibri", serif;font-size:11pt">
<font color="#58595b">Stabilize learning dynamics in deep reinforcement learning </font>
</p>
</li><li>
<p class="western" style="margin-bottom:0cm;line-height:115%;text-align:left;background:transparent none repeat scroll 0% 0%;font-family:"Calibri", serif;font-size:11pt">
<font color="#58595b">Scaling up prior reinforcement learning solutions </font></p>
</li></ul>
</li><li>
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<font color="#58595b">Deep Transfer and multi-task learning: </font></p>
<ul>
<li>
<p class="western" style="margin-bottom:0cm;line-height:115%;text-align:left;background:transparent none repeat scroll 0% 0%;font-family:"Calibri", serif;font-size:11pt">
<font color="#58595b">New perspectives or theories on transfer and multi-task learning
</font></p>
</li><li>
<p class="western" style="margin-bottom:0cm;line-height:115%;text-align:left;background:transparent none repeat scroll 0% 0%;font-family:"Calibri", serif;font-size:11pt">
<font color="#58595b">Dataset bias and concept drift </font></p>
</li><li>
<p class="western" style="margin-bottom:0cm;line-height:115%;text-align:left;background:transparent none repeat scroll 0% 0%;font-family:"Calibri", serif;font-size:11pt">
<font color="#58595b">Transfer learning and domain adaptation </font></p>
</li><li>
<p class="western" style="margin-bottom:0cm;line-height:115%;text-align:left;background:transparent none repeat scroll 0% 0%;font-family:"Calibri", serif;font-size:11pt">
<font color="#58595b">Multi-task learning </font></p>
</li><li>
<p class="western" style="margin-bottom:0cm;line-height:115%;text-align:left;background:transparent none repeat scroll 0% 0%;font-family:"Calibri", serif;font-size:11pt">
<font color="#58595b">Feature based approaches </font></p>
</li><li>
<p class="western" style="margin-bottom:0cm;line-height:115%;text-align:left;background:transparent none repeat scroll 0% 0%;font-family:"Calibri", serif;font-size:11pt">
<font color="#58595b">Instance based approaches </font></p>
</li><li>
<p class="western" style="margin-bottom:0cm;line-height:115%;text-align:left;background:transparent none repeat scroll 0% 0%;font-family:"Calibri", serif;font-size:11pt">
<font color="#58595b">Deep architectures for transfer and multi-task learning </font>
</p>
</li><li>
<p class="western" style="margin-bottom:0cm;line-height:115%;text-align:left;background:transparent none repeat scroll 0% 0%;font-family:"Calibri", serif;font-size:11pt">
<font color="#58595b">Transfer across different architectures, e.g. CNN to RNN </font>
</p>
</li><li>
<p class="western" style="margin-bottom:0cm;line-height:115%;text-align:left;background:transparent none repeat scroll 0% 0%;font-family:"Calibri", serif;font-size:11pt">
<font color="#58595b">Transfer across different modalities, e.g. image to text </font>
</p>
</li><li>
<p class="western" style="margin-bottom:0cm;line-height:115%;text-align:left;background:transparent none repeat scroll 0% 0%;font-family:"Calibri", serif;font-size:11pt">
<font color="#58595b">Transfer across different tasks, e.g. object recognition and detection
</font></p>
</li><li>
<p class="western" style="margin-bottom:0cm;line-height:115%;text-align:left;background:transparent none repeat scroll 0% 0%;font-family:"Calibri", serif;font-size:11pt">
<font color="#58595b">Transfer from weakly labeled or noisy data, e.g. Web data </font>
</p>
</li></ul>
</li><li>
<p class="western" style="margin-bottom:0cm;line-height:115%;text-align:left;background:transparent none repeat scroll 0% 0%;font-family:"Calibri", serif;font-size:11pt">
<font color="#58595b">Datasets, benchmarks, and open-source packages </font></p>
</li><li>
<p class="western" style="margin-bottom:0.25cm;line-height:115%;text-align:left;background:transparent none repeat scroll 0% 0%;font-family:"Calibri", serif;font-size:11pt">
<font color="#58595b">Recourse efficient deep learning </font></p>
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</p>
</li></ul>
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<font color="#58595b"><b><font style="font-size:12pt" size="3">FULL PAPER IMPORTANT DATES</font></b></font></p>
<ul>
<li>
<p style="margin-bottom:0.28cm;line-height:108%;text-align:left;background:transparent none repeat scroll 0% 0%">
<font color="#58595b"><font face="Calibri, serif"><font style="font-size:12pt" size="3"><font color="#ff0000">Full paper submission: August 15th, 2021</font>
</font></font></font></p>
</li><li>
<p style="margin-bottom:0.28cm;line-height:108%;text-align:left;background:transparent none repeat scroll 0% 0%">
<font color="#58595b"><font style="font-size:12pt" size="3">Full paper acceptance notification: September
</font><font face="Calibri, serif"><font style="font-size:12pt" size="3"><span lang="en-AE">30th</span></font></font><font style="font-size:12pt" size="3">, 2021</font></font></p>
</li><li>
<p style="margin-bottom:0.28cm;line-height:108%;text-align:left;background:transparent none repeat scroll 0% 0%">
<font color="#58595b"><font style="font-size:12pt" size="3">Full paper camera-ready submission: October
</font><font style="font-size:12pt" size="3">20</font><font style="font-size:12pt" size="3">th, 2021</font></font></p>
</li></ul>
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<font color="#58595b"><b><font style="font-size:12pt" size="3">Submission Site:</font></b></font></p>
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<font color="#58595b"><font style="font-size:12pt" size="3"><a href="https://easychair.org/conferences/?conf=dtl2021">https://easychair.org/conferences/?conf=dtl2021</a></font></font></p>
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<font color="#58595b"><b><font style="font-size:12pt" size="3">Paper format</font></b></font></p>
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<font color="#58595b"><font style="font-size:12pt" size="3">Submitted papers (.pdf format) must use
<a href="https://www.ieee.org/conferences/publishing/templates.html">the A4 IEEE Manuscript Templates for Conference Proceedings</a>. Please remember to add Keywords to your submission.
</font></font></p>
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<font color="#58595b"><b><font style="font-size:12pt" size="3">Length</font></b></font></p>
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<font color="#58595b"><font style="font-size:12pt" size="3">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.</font></font></p>
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<font color="#58595b"><b><font style="font-size:12pt" size="3">Originality</font></b></font></p>
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<font color="#58595b"><font style="font-size:12pt" size="3">Papers submitted to IDSTA must be the original work of the authors. The 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 IDSTA. 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.</font></font></p>
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<font color="#58595b"><b><font style="font-size:12pt" size="3">Publication:</font></b></font></p>
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<font color="#58595b"><font style="font-size:12pt" size="3">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.</font></font></p>
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Best regards,</p>
<p class="western" style="margin-bottom:0cm;line-height:115%;text-align:left;background:transparent none repeat scroll 0% 0%;font-family:"Calibri", serif;font-size:11pt">
IDSTA organising committee</p>
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