<html><head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class=""><meta http-equiv="Content-Type" content="text/html; charset=utf-8" class=""><div style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class=""><div class=""><div class="">** Apologies for cross-posting** CFP [Deadline extended]:</div><div class=""><br class=""></div></div><div class="">Special Session on "Learning Representations for Structured Data"</div><div class="">2019 International Joint Conference on Neural Networks (IJCNN)</div><div class="">July 14-19 2019, Budapest, Hungary</div><div class=""><a href="https://sites.google.com/view/lr4sd-ijcnn19" class="">https://sites.google.com/view/lr4sd-ijcnn19</a></div><div class=""><br class=""></div><div class="">Important Dates:</div><div class=""><div class="">Paper submission: EXTENDED to 15 January 2019</div><div class="">Notification of acceptance: 28 February 2019</div></div><div class=""><br class=""></div><div class="">Aims and Scope:</div><div class="">Structured data, e.g. sequences, trees and graphs, are a natural representation for compound information made of atomic information pieces (i.e. the nodes and their labels) and their intertwined relationships, represented by the edges (and their labels). Sequences are simple structures representing linear dependencies such as in genomics and proteomics, or with time series data. Trees, on the other hand, allow to model hierarchical contexts and relationships, such as with natural language sentences, crystallographic structures, images. Graphs are the most general and complex form of structured data allowing to represent networks of interacting elements, e.g. in social graphs or metabolomics, as well as data where topological variations influence the feature of interest, e.g. molecular compounds. Being able to process data in these rich structured forms provides a fundamental advantage when it comes to identifying data patterns suitable for predictive and/or explorative analyses. This has motivated a recent increasing interest of the machine learning community into the development of learning models for structured information. </div><div class="">On the other hand, recent improvements in the predictive performances shown by machine learning methods is due to their ability, in contrast to traditional approaches, to learn a “good” representation for the task under consideration. Deep Learning techniques are nowadays widespread, since they allow to perform such representation learning in an end-to-end fashion. Nonetheless, representations learning is becoming of great importance in other areas, such in kernel-based and probabilistic models. It has also been shown that, when the data available for the task at hand is limited, it is still beneficial to resort to representations learned in an unsupervised fashion, or on different, but related, tasks.</div><div class="">This session focuses on learning representation for structured data such as sequences, trees, graphs, and relational data. </div><div class=""><br class=""></div><div class="">Topics that are of interest to this session include, but are not limited to:</div><div class="">- Probabilistic models for structured data</div><div class="">- Structured output generation (probabilistic models, variational autoencoders, adversarial training, …)</div><div class="">- Deep learning and representation learning for structures</div><div class="">- Learning with network data</div><div class="">- Recurrent, recursive and contextual models</div><div class="">- Reservoir computing and randomized neural networks for structures</div><div class="">- Kernels for structured data</div><div class="">- Relational deep learning</div><div class="">- Learning implicit representations</div><div class="">- Applications of adaptive structured data processing: e.g. Natural Language Processing, machine vision (e.g. point clouds as graphs), materials science, chemoinformatics, computational biology, social networks.</div><div class=""><br class=""></div><div class="">Submission:</div><div class="">- For paper guidelines please visit <a href="https://www.ijcnn.org/paper-submission-guidelines" class="">https://www.ijcnn.org/paper-submission-guidelines</a></div><div class="">- For submissions please select the single topic "S11. Learning Representations for Structured Data" from the "S. SPECIAL SESSIONS" category as the main research topic on <a href="https://ieee-cis.org/conferences/ijcnn2019/upload.php" class="">https://ieee-cis.org/conferences/ijcnn2019/upload.php</a></div><div class=""><br class=""></div><div class="">Organisers:</div><div class="">- Davide Bacciu, University of Pisa</div><div class="">- Thomas Gärtner, University of Nottingham</div><div class="">- Nicolò Navarin, University of Padova</div><div class="">- Alessandro Sperduti, University of Padova</div><div class="">For any enquire, please write to: bacciu [at] <a href="http://di.unipi.it/" class="">di.unipi.it</a> or nnavarin [at] <a href="http://math.unipd.it/" class="">math.unipd.it</a></div><div class=""><br class=""></div><div class="">
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