posted by user: nickgentoo || 909 views || tracked by 4 users: [display]

LR4SD 2019 : CFP Special Session on Learning Representations for Structured Data (LR4SD) @ IJCNN 2019

FacebookTwitterLinkedInGoogle

Link: https://sites.google.com/view/lr4sd-ijcnn19
 
When Jul 14, 2019 - Jul 19, 2019
Where Budapest, Hungary
Submission Deadline Jan 15, 2019
Notification Due Feb 28, 2019
Categories    learning representations   structured data   deep learning   neural networks
 

Call For Papers

** Apologies for cross-posting** CFP [Deadline extended]:

Special Session on "Learning Representations for Structured Data"
2019 International Joint Conference on Neural Networks (IJCNN)
July 14-19 2019, Budapest, Hungary
https://sites.google.com/view/lr4sd-ijcnn19

Important Dates:
Paper submission: EXTENDED to 15 January 2019
Notification of acceptance: 28 February 2019

Aims and Scope:
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.
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.
This session focuses on learning representation for structured data such as sequences, trees, graphs, and relational data.

Topics that are of interest to this session include, but are not limited to:
- Probabilistic models for structured data
- Structured output generation (probabilistic models, variational autoencoders, adversarial training, …)
- Deep learning and representation learning for structures
- Learning with network data
- Recurrent, recursive and contextual models
- Reservoir computing and randomized neural networks for structures
- Kernels for structured data
- Relational deep learning
- Learning implicit representations
- 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.

Submission:
- For paper guidelines please visit https://www.ijcnn.org/paper-submission-guidelines
- 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 https://ieee-cis.org/conferences/ijcnn2019/upload.php

Organisers:
- Davide Bacciu, University of Pisa
- Thomas Gärtner, University of Nottingham
- Nicolò Navarin, University of Padova
- Alessandro Sperduti, University of Padova
For any enquire, please write to: bacciu [at] di.unipi.it or nnavarin [at] math.unipd.it

Related Resources

ICLR 2019   International Conference for Learning Representations
ICMLA 2019   18th IEEE International Conference on Machine Learning and Applications
ISBDAI 2020   【Ei Compendex Scopus】2018 International Symposium on Big Data and Artificial Intelligence
EWRE-EI/Scopus 2019   2019 2nd International Conference on Environmental and Water Resources Engineering
GridCom 2019   11th International Conference on Grid Computing
ISPA - 3DSI&SLS 2019   ISPA2019 - Special Session on 3D Surface Imaging and Structured Light Scanning
IDEAL 2019   Intelligent Data Engineering and Automated Learning
ETRIJ 5G&B5G 2020   ETRI Journal Special Issue on 5G & B5G Enabling Edge Computing, Big Data & Deep Learning Technologies
Machine Learning and Applications 2019   Machine Learning and Applications: An International Journal
Hand Book on Big Data Analytics 2020   IET Handbooks on Big Data Analytics