<div dir="ltr"><br><div class="gmail_quote"><div dir="ltr">
<table class="m_7880924494136158486gmail-MsoNormalTable" style="width:100%" width="100%" cellpadding="0" border="0">
<tbody><tr>
<td style="padding:0.75pt">
<p class="MsoNormal" style="margin:0in 0in 0.0001pt;text-align:center;line-height:normal;font-size:11pt;font-family:"Calibri",sans-serif" align="center"><b><span style="font-size:13.5pt;font-family:"Times New Roman",serif;color:rgb(0,0,64)">The First International Workshop on Deep and Transfer Learning (DTL 2018) <span></span></span></b></p>
<p class="MsoNormal" style="text-align:center;line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif" align="center"><a href="http://emergingtechnet.org/DTL2018/default.php" style="color:blue;text-decoration:underline" target="_blank"><b><span style="font-size:13.5pt;font-family:"Times New Roman",serif">http://emergingtechnet.org/<wbr>DTL2018/default.php</span></b></a><b><span style="font-size:13.5pt;font-family:"Times New Roman",serif;color:rgb(0,0,64)"><span></span></span></b></p>
<p class="MsoNormal" style="text-align:center;line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif" align="center"><b><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,64)">in conjunction with</span></b><span style="font-size:12pt;font-family:"Times New Roman",serif"><span></span></span></p>
<p class="MsoNormal" style="text-align:center;line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif" align="center"><a href="http://emergingtechnet.org/IoTSMS2018/index.php" style="color:blue;text-decoration:underline" target="_blank"><b><span style="font-size:13.5pt;font-family:"Times New Roman",serif">The Fifth International Conference on Internet of Things:
Systems, Management and Security (IoTSMS 2018)</span></b></a><b><span style="font-size:13.5pt;font-family:"Times New Roman",serif;color:rgb(0,0,64)"><br>
<br>
Valencia, Spain. October 15-18, 2018</span></b><span style="font-size:12pt;font-family:"Times New Roman",serif">
<span></span></span></p>
</td>
<td style="padding:0.75pt">
<p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif"> <span></span></span></p>
</td>
</tr>
</tbody></table>
<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. 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.<span></span></span></p>
<p class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><b><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,64)">Topics of interest </span></b><b><span style="font-size:12pt;font-family:"Times New Roman",serif"><br>
</span></b><span style="font-size:12pt;font-family:"Times New Roman",serif">============== <br>
Authors are encouraged to submit their original work, which is not submitted
elsewhere, to this workshop. The topics of the workshop include but not limited
to:<span></span></span></p>
<ul style="margin-bottom:0in" type="disc">
<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>
<p class="MsoNormal" style="margin:0in 0in 0.0001pt 0.5in;line-height:normal;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif"><span> </span>o Deep transfer
learning for robots<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in 0.0001pt 0.5in;line-height:normal;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif"><span> </span>o Determining rewards
for machines<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in 0.0001pt 0.5in;line-height:normal;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif"><span> </span>o Machine
translation<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in 0.0001pt 0.5in;line-height:normal;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif"><span> </span><span> </span>o Energy consumption issues in deep
reinforcement learning<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif"><span> </span><span> </span>o Deep reinforcement learning for game
playing<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in 0.0001pt 0.5in;line-height:normal;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif"><span> </span><span> </span>o Stabilize learning dynamics in deep
reinforcement learning<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in 0.0001pt 0.5in;line-height:normal;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif"><span> </span>o Scaling up prior
reinforcement learning solutions<span></span></span></p>
<ul style="margin-top:0in;margin-bottom:0in" type="disc">
<li class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;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>
<p class="MsoNormal" style="margin:0in 0in 0.0001pt 0.5in;line-height:normal;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif"><span> </span>o New perspectives
or theories on transfer and multi-task learning<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in 0.0001pt 0.5in;line-height:normal;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif"><span> </span>o Dataset bias and
concept drift<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in 0.0001pt 0.5in;line-height:normal;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif"><span> </span>o Transfer
learning and domain adaptation<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in 0.0001pt 0.5in;line-height:normal;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif"><span> </span>o Multi-task
learning<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in 0.0001pt 0.5in;line-height:normal;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif"><span> </span>o Feature based
approaches<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in 0.0001pt 0.5in;line-height:normal;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif"><span> </span>o Instance based
approaches<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in 0.0001pt 0.5in;line-height:normal;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif"><span> </span>o Deep
architectures for transfer and multi-task learning<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in 0.0001pt 0.5in;line-height:normal;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif"><span> </span>o Transfer across
different architectures, e.g. CNN to RNN<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in 0.0001pt 0.5in;line-height:normal;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif"><span> </span>o Transfer across
different modalities, e.g. image to text<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in 0.0001pt 0.5in;line-height:normal;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif"><span> </span>o Transfer across
different tasks, e.g. object recognition and detection<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in 0.0001pt 0.5in;line-height:normal;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif"><span> </span>o Transfer from
weakly labeled or noisy data, e.g. Web data<span></span></span></p>
<ul style="margin-top:0in;margin-bottom:0in" type="disc">
<li class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;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>
</ul>
<p class="MsoNormal" style="margin:0in 0in 0.0001pt 0.5in;line-height:normal;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif"><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><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,64)">Paper Submission : </span></b><a name="m_7880924494136158486__submission"><span style="font-size:12pt;font-family:"Times New Roman",serif"><br>
=============<span></span></span></a></p>
<p class="MsoNormal" style="margin:0in 0in 0.0001pt;text-align:justify;line-height:normal;font-size:11pt;font-family:"Calibri",sans-serif"><span><span style="font-size:12pt;font-family:"Times New Roman",serif">Authors are requested to submit papers reporting original
research results and experience. The page limit for full papers is 6 pages.
Papers should be prepared using IEEE two-column template. <span></span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in 0.0001pt;text-align:justify;line-height:normal;font-size:11pt;font-family:"Calibri",sans-serif"><span><span style="font-size:12pt;font-family:"Times New Roman",serif"><span> </span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in 0.0001pt;text-align:justify;line-height:normal;font-size:11pt;font-family:"Calibri",sans-serif"><span><span style="font-size:12pt;font-family:"Times New Roman",serif">IEEE Computer Society Proceedings Author Guidelines are
available at: </span></span><a href="http://www.computer.org/portal/web/cscps/submission" style="color:blue;text-decoration:underline" target="_blank"><span><span style="font-size:12pt;font-family:"Times New Roman",serif">IEEE
Guidelines Link</span></span></a><span><span style="font-size:12pt;font-family:"Times New Roman",serif"> <span></span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in 0.0001pt;text-align:justify;line-height:normal;font-size:11pt;font-family:"Calibri",sans-serif"><span><span style="font-size:12pt;font-family:"Times New Roman",serif">Papers should be submitted as PDF files via the EasyChair: </span></span><a href="https://easychair.org/conferences/?conf=anlp2018" style="color:blue;text-decoration:underline" target="_blank"><span><span style="font-size:12pt;font-family:"Times New Roman",serif">EasyChair Link</span></span></a><span></span><span style="font-size:12pt;font-family:"Times New Roman",serif">
<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in 0.0001pt;text-align:justify;line-height:normal;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;font-family:"Times New Roman",serif">Submitted
research papers may not overlap with papers that have already been published or
that are simultaneously submitted to a journal or a conference. All papers
accepted for this conference are peer-reviewed and are to be published in the IoTSMS
conference proceedings and will be submitted for inclusion in IEEE Xplore
Digital Library. <br></span></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;text-align:justify;line-height:normal;font-size:11pt;font-family:"Calibri",sans-serif"><br><span style="font-size:12pt;font-family:"Times New Roman",serif"><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><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,64)">Important Dates : </span></b><a name="m_7880924494136158486__dates"></a><span style="font-size:12pt;font-family:"Times New Roman",serif"><br>
=============<span></span></span></p>
<p class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><b><span style="font-size:12pt;font-family:"Times New Roman",serif">Full Paper Submission:</span></b><span style="font-size:12pt;font-family:"Times New Roman",serif"> July 15, 2018 <br>
<b>Notification of Decision:</b> August 25th, 2018<br>
<b>Camera-Ready and Registration :</b> September 5th, 2018<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:"Calibri",sans-serif"><span style="font-size:12pt;line-height:107%;font-family:"Times New Roman",serif">All
questions about submissions should be emailed to: <a href="mailto:emergingtechnetwork@gmail.com" target="_blank">emergingtechnetwork@gmail.com</a><span></span></span></p>
<br></div>
</div><br></div>