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<p class="MsoNormal"><span lang="EN-US" class="" style="font-size: 11pt;">[Apologies if you receive multiple copies of this CFP]</span></p>
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<p class="MsoNormal"><b class=""><span lang="EN-US" class="" style="font-size: 11pt;">Call for papers: special session on "Parallel and Distributed Machine Learning: Theory and Applications" at ESANN 2019</span></b></p>
<p class="MsoNormal"><span lang="EN-US" class="" style="font-size: 11pt;">European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2019)<o:p class=""></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" class="" style="font-size: 11pt;">24-26 April 2019, Bruges (Belgium) - <a href="http://www.esann.org" class="">http://www.esann.org</a><o:p class=""></o:p></span></p>
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<p class="MsoNormal"><b class=""><span lang="EN-US" class="" style="font-size: 11pt;">Parallel and Distributed Machine Learning: Theory and Applications</span></b></p>
<p class="MsoNormal"><i class=""><span class="" style="font-size: 11pt;">Organized by: Beatriz Remeseiro (Universidad de Oviedo, Spain), Verónica Bolón-Canedo, Jorge González-Domínguez, Amparo Alonso-Betanzos (Universidade da Coruña, Spain) <o:p class=""></o:p></span></i></p>
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<p class="MsoNormal" style="text-align: justify;"><span lang="EN-US" class="" style="font-size: 11pt;">The spread of Internet and the technological advances have resulted in huge volumes of data, very valuable for different agents in the industrial world that
are interested in analyzing them for different purposes. Machine Learning (ML) algorithms play a key role in this context, being able to learn from and make predictions on data. Their increasing complexity, since they have to deal now with millions of parameters,
as well as their computational cost lead to new research opportunities and technical challenges.</span></p>
<p class="MsoNormal" style="text-align: justify;"><span lang="EN-US" class="" style="font-size: 11pt;">This continuous increase of data involved in ML analyses leads to a growing interest in the design and implementation of parallel and distributed ML algorithms.
The efficient exploitation of the vast aggregate main memory and processing power of High Performance Computing (HPC) resources such as multicore CPUs, hardware accelerators (GPUs, Intel Xeon Phi coprocessors, FPGAs, etc.), clusters or cloud-based systems
can significantly accelerate many ML algorithms. However, the development of efficient parallel algorithms is not trivial, as we must pay much attention to the data organization and decomposition strategy in order to balance the workload among resources while
minimizing data dependencies as well as synchronization and communication overhead.</span><span class="" style="font-size: 11pt;"> </span></p>
<p class="MsoNormal" style="text-align: justify;"><span lang="EN-US" class="" style="font-size: 11pt;">We invite papers on both practical and theoretical issues about incorporating parallel and distributed approaches into ML problems, as well as review papers
with the state-of-art techniques and the open challenges encountered in this field. In particular, topics of interest include, but are not limited to:</span></p>
<p class="MsoNormalCxSpMiddle" style="margin-left: 36pt; text-align: justify; text-indent: -18pt; line-height: 13.800000190734863px;">
<span lang="EN-US" class="" style="font-size: 11pt; line-height: 16.866666793823242px;">-<span class="" style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><span lang="EN-US" class="" style="font-size: 11pt; line-height: 16.866666793823242px;">Development
of parallel ML algorithms on multicore and manycore architectures: multithreading, GPUs, Intel Xeon Phi coprocessor, FPGAs, etc.<o:p class=""></o:p></span></p>
<p class="MsoNormalCxSpMiddle" style="margin-left: 36pt; text-align: justify; text-indent: -18pt; line-height: 13.800000190734863px;">
<span lang="EN-US" class="" style="font-size: 11pt; line-height: 16.866666793823242px;">-<span class="" style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><span lang="EN-US" class="" style="font-size: 11pt; line-height: 16.866666793823242px;">Exploitation
of cloud, grid and distributed-memory systems to accelerate ML algorithms: Spark, Hadoop, MPI, etc.<o:p class=""></o:p></span></p>
<p class="MsoNormalCxSpMiddle" style="margin-left: 36pt; text-align: justify; text-indent: -18pt; line-height: 13.800000190734863px;">
<span lang="EN-US" class="" style="font-size: 11pt; line-height: 16.866666793823242px;">-<span class="" style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><span lang="EN-US" class="" style="font-size: 11pt; line-height: 16.866666793823242px;">Deep
learning models trained across multicore CPUs, GPUs or clusters of computers.<o:p class=""></o:p></span></p>
<p class="MsoNormalCxSpMiddle" style="margin-left: 36pt; text-align: justify; text-indent: -18pt; line-height: 13.800000190734863px;">
<span lang="EN-US" class="" style="font-size: 11pt; line-height: 16.866666793823242px;">-<span class="" style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><span lang="EN-US" class="" style="font-size: 11pt; line-height: 16.866666793823242px;">Development
of distributed ML algorithms.<o:p class=""></o:p></span></p>
<p class="MsoNormalCxSpMiddle" style="margin-left: 36pt; text-align: justify; text-indent: -18pt; line-height: 13.800000190734863px;">
<span lang="EN-US" class="" style="font-size: 11pt; line-height: 16.866666793823242px;">-<span class="" style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><span lang="EN-US" class="" style="font-size: 11pt; line-height: 16.866666793823242px;">Novel
programming paradigms to support HPC for ML. <o:p class=""></o:p></span></p>
<p class="MsoNormalCxSpMiddle" style="margin-left: 36pt; text-align: justify; text-indent: -18pt; line-height: 13.800000190734863px;">
<span lang="EN-US" class="" style="font-size: 11pt; line-height: 16.866666793823242px;">-<span class="" style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><span lang="EN-US" class="" style="font-size: 11pt; line-height: 16.866666793823242px;">Middleware,
programming models, tools, and environments for HPC in ML.<o:p class=""></o:p></span></p>
<p class="MsoNormalCxSpMiddle" style="margin-left: 36pt; text-align: justify; text-indent: -18pt; line-height: 13.800000190734863px;">
<span lang="EN-US" class="" style="font-size: 11pt; line-height: 16.866666793823242px;">-<span class="" style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><span lang="EN-US" class="" style="font-size: 11pt; line-height: 16.866666793823242px;">Caching,
streaming, pipelining, and other optimization techniques for data management in HPC for ML.<o:p class=""></o:p></span></p>
<p class="MsoNormalCxSpMiddle" style="margin-left: 36pt; text-align: justify; text-indent: -18pt; line-height: 13.800000190734863px;">
<span lang="EN-US" class="" style="font-size: 11pt; line-height: 16.866666793823242px;">-<span class="" style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><span lang="EN-US" class="" style="font-size: 11pt; line-height: 16.866666793823242px;">Benchmarking
and performance studies of high-performance ML applications.<o:p class=""></o:p></span></p>
<p class="MsoNormalCxSpMiddle" style="margin-left: 36pt; text-align: justify; text-indent: -18pt; line-height: 13.800000190734863px;">
<span lang="EN-US" class="" style="font-size: 11pt; line-height: 16.866666793823242px;">-<span class="" style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><span lang="EN-US" class="" style="font-size: 11pt; line-height: 16.866666793823242px;">Parallel
databases and I/O systems to store ML data.<o:p class=""></o:p></span></p>
<p class="MsoNormalCxSpMiddle" style="margin-left: 36pt; text-align: justify; text-indent: -18pt; line-height: 13.800000190734863px;">
<span lang="EN-US" class="" style="font-size: 11pt; line-height: 16.866666793823242px;">-<span class="" style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><span lang="EN-US" class="" style="font-size: 11pt; line-height: 16.866666793823242px;">Applications
and services: bioinformatics, medicine, multimedia, video surveillance, etc.<o:p class=""></o:p></span></p>
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<p class="MsoNormal"><span lang="EN-US" class="" style="font-size: 11pt;">Submitted papers will be reviewed according to the ESANN reviewing process and will be evaluated on their scientific value: originality, correctness, and writing style.<o:p class=""></o:p></span></p>
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<p class="MsoNormal"><b class=""><span lang="EN-US" class="" style="font-size: 11pt;">IMPORTANT DATES:<o:p class=""></o:p></span></b></p>
<p class="MsoNormal"><span lang="EN-US" class="" style="font-size: 11pt;">Paper submission deadline: 26 November 2018<o:p class=""></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" class="" style="font-size: 11pt;">Notification of acceptance: 31 January 2019<o:p class=""></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" class="" style="font-size: 11pt;">ESANN conference: 24-26 April 2019</span></p>
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