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<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Times New Roman",serif">Dear colleague,<o:p></o:p></span></p>
<p class="m-8640046564942656245m-9201808155033497731gmail-msoplaintext">The 6th International Workshop for Parallel and Distributed Computing for Large Scale Machine
<span class="il">Learning</span> and Big Data Analytics (<span class="il">ParLearning</span>) will be held with
<a href="http://ipdps.org/" target="_blank">IEEE IPDPS</a> on May 29, 2017 in Orlando, Florida, USA. As in previous years, we invite novel works that advance the fields of Machine
<span class="il">Learning</span>, Data Mining, and Artificial Intelligence through development of scalable parallel algorithms or distributed computing frameworks. Ideal submissions would be characterized as scaling up
<i>X</i> on <i>Y</i>, where potential choices for <i>X</i> and <i>Y</i> are provided below.
<o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span style="font-size:12.0pt;font-family:"Times New Roman",serif">Scaling up<o:p></o:p></span></p>
<ul type="disc">
<li class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l2 level1 lfo3">
<span style="font-size:12.0pt;font-family:"Times New Roman",serif">recommender systems<o:p></o:p></span></li><li class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l2 level1 lfo3">
<span style="font-size:12.0pt;font-family:"Times New Roman",serif">gradient descent algorithms<o:p></o:p></span></li><li class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l2 level1 lfo3">
<span style="font-size:12.0pt;font-family:"Times New Roman",serif">deep learning<o:p></o:p></span></li><li class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l2 level1 lfo3">
<span style="font-size:12.0pt;font-family:"Times New Roman",serif">sampling/sketching techniques<o:p></o:p></span></li><li class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l2 level1 lfo3">
<span style="font-size:12.0pt;font-family:"Times New Roman",serif">clustering (agglomerative techniques, graph clustering, clustering heterogeneous data)<o:p></o:p></span></li><li class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l2 level1 lfo3">
<span style="font-size:12.0pt;font-family:"Times New Roman",serif">classification (SVM and other classifiers)<o:p></o:p></span></li><li class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l2 level1 lfo3">
<span style="font-size:12.0pt;font-family:"Times New Roman",serif">SVD<o:p></o:p></span></li><li class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l2 level1 lfo3">
<span style="font-size:12.0pt;font-family:"Times New Roman",serif">probabilistic inference (Bayesian networks)<o:p></o:p></span></li><li class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l2 level1 lfo3">
<span style="font-size:12.0pt;font-family:"Times New Roman",serif">logical reasoning<o:p></o:p></span></li><li class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l2 level1 lfo3">
<span style="font-size:12.0pt;font-family:"Times New Roman",serif">graph algorithms and graph mining<o:p></o:p></span></li></ul>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span style="font-size:12.0pt;font-family:"Times New Roman",serif">On<o:p></o:p></span></p>
<ul type="disc">
<li class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l1 level1 lfo6">
<span style="font-size:12.0pt;font-family:"Times New Roman",serif">Multi-core architectures/frameworks (OpenMP)<o:p></o:p></span></li><li class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l1 level1 lfo6">
<span style="font-size:12.0pt;font-family:"Times New Roman",serif">Many-core (GPU) architectures/frameworks (OpenCL, OpenACC, CUDA, Intel TBB)<o:p></o:p></span></li><li class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l1 level1 lfo6">
<span style="font-size:12.0pt;font-family:"Times New Roman",serif">Distributed systems/frameworks (GraphLab, MPI, Hadoop, Spark, Storm, Mahout, etc.)<o:p></o:p></span></li></ul>
<p class="m-8640046564942656245m-9201808155033497731gmail-msoplaintext">All papers will be included in the IEEE IPDPS workshops proceedings (and included in the IEEE Xplore Digital Library after the conference). Selected papers from the workshop will be published
in a Special Issue of Future Generation Computer Systems, Elsevier's International Journal of eScience.<o:p></o:p></p>
<p class="m-8640046564942656245m-9201808155033497731gmail-msoplaintext">The Call for Papers and submission details are at the workshop webpage:
<a href="http://parlearning.ecs.fullerton.edu/" target="_blank">http://<span class="il"><span style="color:blue">parlearning</span></span>.ecs.fullerton.edu/</a>
<o:p></o:p></p>
<p class="m-8640046564942656245m-9201808155033497731gmail-msoplaintext">Sincerely,
<o:p></o:p></p>
<p class="m-8640046564942656245m-9201808155033497731gmail-msoplaintext" style="margin-bottom:12.0pt">
Anand Panangadan, PhD<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
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