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DLID 2019 : 2nd Deep Learning on Irregular Domains Workshop at IEEE ICDM'19


When Nov 8, 2019 - Nov 8, 2019
Where Beijing, China
Submission Deadline Aug 7, 2019
Notification Due Sep 4, 2019
Final Version Due Sep 8, 2019
Categories    deep learning   machine learning   graph   data mining

Call For Papers

We are pleased to introduce the 2nd workshop on Deep Learning on Irregular Domains (DLID), hosted at the 19th IEEE International Conference on Data Mining, hosted in Beijing, China.


Advances in learning spatially related features via convolutional neural network (CNN) architectures has resulted in strong performance gains within the image understanding domain. Many real world problems do not exhibit such a regular spatial domain, making it non-trivial to define a feature mining operator. Such domains may still exhibit spatial relationships that may be of use for learning; weather stations across a country, or joints on the human skeleton for example. The area of deep learning on irregular domains has attempted to make use of the intrinsic spatial information encoded in the domain to learn features on the problem at hand. They employ such methods as signal processing on the graph, graph-based CNNs and manifold-based heat kernels to learn a filtering on input data from a localised region of the domain.

This workshop aims to foster study into the understanding of implementing deep learning on spaces in which conventional CNN operations are ill-defined. It is hoped that the community will be able to engage in dedicated discussions into advancing state of the art performance for problem domains with an irregular topology.


DLID is currently accepting submissions from a wide range of topics, including:

* Exploiting spatial relationships in non-Euclidean domains
* Data mining and signal processing on graphs
* Spectral graph methods
* Deep learning on irregular problems and graph structured data
* Feature extraction on graphs
* Applications of deep learning on novel domains
* Developing filters on manifolds, graphs and non-Euclidean spaces
* Learning domain topology
* Graph construction and pooling
* Domains with an irregular spatial relationships that would benefit from feature mining.

Alongside method papers, we also encourage submissions concerning application of such methods. This includes, but is not limited to, such domains as:

* Medical image analysis
* Text analysis
* Social media analysis
* Human action recognition
* Sensor network analysis


Paper Submission: 7th August 2019
Author Notification: 4th September 2019
Camera Ready Submission: 8th September 2019
Workshop Event: 8th November 2019

For further information regarding the DLID workshop, please visit:

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