<div dir="ltr"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">Apologies for cross-posting. Kindly help to distribute this call to your mailing list.</span><br style="text-decoration-style:initial;text-decoration-color:initial"><br style="text-decoration-style:initial;text-decoration-color:initial"><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">INNS BIG DATA AND DEEP LEARNING 2019</span><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">SESTRI LEVANTE, GENOA, ITALY, 16-18 APRIL 2019</span><br style="text-decoration-style:initial;text-decoration-color:initial"><a href="https://innsbddl2019.org/" style="color:rgb(17,85,204)" target="_blank">HTTPS://INNSBDDL2019.ORG/</a><br style="text-decoration-style:initial;text-decoration-color:initial"><br style="text-decoration-style:initial;text-decoration-color:initial"><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">WEBSITE<span> </span></span><br style="text-decoration-style:initial;text-decoration-color:initial"><br style="text-decoration-style:initial;text-decoration-color:initial"><a href="https://innsbddl2019.org/" style="color:rgb(17,85,204)" target="_blank">https://innsbddl2019.org/</a><br style="text-decoration-style:initial;text-decoration-color:initial"><br style="text-decoration-style:initial;text-decoration-color:initial"><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">VENUE & ORGANIZATION</span><br style="text-decoration-style:initial;text-decoration-color:initial"><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">The 2019 INNS Big Data and Deep Learning (</span><span class="m_-7048174159493902673gmail-il" style="text-decoration-style:initial;text-decoration-color:initial">INNSBDDL</span><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline"><span> </span>2019) conference will be held in Sestri Levante, Italy, April 16–18, 2019. The conference is organized by the International Neural Network Society, with the aim of representing an international meeting for researchers and other professionals in Big Data, Deep Learning and related areas. It will feature invited plenary talks by world-renowned speakers in the area, in addition to regular and special technical sessions with oral and poster presentations. Moreover, workshops and tutorials will also be featured.</span><br style="text-decoration-style:initial;text-decoration-color:initial"><br style="text-decoration-style:initial;text-decoration-color:initial"><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">IMPORTANT DATES</span><br style="text-decoration-style:initial;text-decoration-color:initial"><br style="text-decoration-style:initial;text-decoration-color:initial"><div>Deadline for submission of tutorial and workshop proposals: August 31, 2018</div><div>Notification of tutorial and workshop proposals: September 30, 2018</div><div>Deadline of full paper submission: October 31, 2018</div><div>Notification of paper acceptance: December 31, 2018</div><div>Camera-ready submission: January 31, 2019</div><div>Early registration deadline: January 15, 2019</div><div>Registration deadline: January 31, 2019</div><div>Conference date: April 16-18, 2019</div><br style="text-decoration-style:initial;text-decoration-color:initial"><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">SCOPE</span><br style="text-decoration-style:initial;text-decoration-color:initial"><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">We solicit both solid contributions or preliminary results which show the potentiality and the limitations of new ideas, refinements, or contaminations in any aspect of Big Data and Deep Learning. Both theoretical and practical results are welcome.</span><br style="text-decoration-style:initial;text-decoration-color:initial"><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">Example topics of interest includes but is not limited to the following:</span><br style="text-decoration-style:initial;text-decoration-color:initial"><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">Big Data Science and Foundations</span><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">- Novel Theoretical Models for Big Data</span><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">- New Computational Models for Big Data</span><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">- Data and Information Quality for Big Data</span><br style="text-decoration-style:initial;text-decoration-color:initial"><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">Big Data Mining</span><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">- Social Web Mining</span><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">- Data Acquisition, Integration, Cleaning, and Best Practices</span><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">- Visualization Analytics for Big Data</span><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">- Computational Modeling and Data Integration</span><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">- Large-scale Recommendation Systems and Social Media Systems</span><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">- Cloud/Grid/StreamData Mining</span><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">- Big Velocity Data</span><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">- Link and Graph Mining</span><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">- Semantic-based Data Mining and Data Pre-processing</span><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">- Mobility and Big Data</span><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">- Multimedia and Multi-structured Data-Big Variety Data</span><br style="text-decoration-style:initial;text-decoration-color:initial"><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">Modern Practical Deep Networks</span><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">- Deep Feedforward Networks</span><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">- Regularization for Deep Learning</span><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">- Optimization for Training Deep Models</span><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">- Convolutional Networks</span><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">- Sequence Modeling: Recurrent and Recursive Nets</span><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">- Practical Methodology</span><br style="text-decoration-style:initial;text-decoration-color:initial"><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">Deep Learning Research</span><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">- Linear Factor Models</span><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">- Autoencoders</span><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">- Representation Learning</span><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">- Structured Probabilistic Models for Deep Learning</span><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">- Monte Carlo Methods</span><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">- Confronting the Partition Function</span><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">- Approximate Inference</span><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">- Deep Generative Models</span><br style="text-decoration-style:initial;text-decoration-color:initial"><br style="text-decoration-style:initial;text-decoration-color:initial"><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">PROCEEDINGS & SPECIAL ISSUE</span><br style="text-decoration-style:initial;text-decoration-color:initial"><br style="text-decoration-style:initial;text-decoration-color:initial"><span style="text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">Works submitted as a regular paper will be published in a serie indexed by Scopus. Submitted papers will be reviewed by some PC members based on technical quality, relevance, originality, significance and clarity. At least one author of an accepted submission should register to present their work at the conference. Selected papers presented at INNS BDDL 2019 will be included in special issues of top journals in the field (prospected journals: Big Data Research, Transaction on Neural Networks and Learning System, Neurocomputing, etc).</span><br></div>