INNSBDDL 2019 - 2019 INNS Big Data and Deep Learning (INNSBDDL 2019) conference

The 2019 INNS Big Data and Deep Learning (INNSBDDL 2019) conference will be held in Sestri Levante, Italy, April 16 ñ 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.
###Invited Speakers###
* Hava Siegelmann, DARPA, USA
* Paolo Ferragina, University of Pisa, Italy
* Guang-Bin Huang, Nanyang Technological University, Singapore
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###Tutorials###
* Alessio Micheli (University of Pisa), Davide Bacciu (University of Pisa), Deep Learning for Graphs
* Silvia Chiappa (DeepMind), Luca Oneto (University of Genoa), Fairness in Machine Learning
* Claudio Gallicchio (University of Pisa), Simone Scardapane (Sapienza University of Rome), Deep Randomized Neural Networks
* Věra Kůrková (Czech Academy of Sciences), Complexity of Shallow and Deep Networks
* Danilo P. Mandic, Ilia Kisil, and Giuseppe G. Calvi (Imperial College London), Tensor Decompositions and Applications. Blessing of Dimensionality
* German I. Parisi and Stefan Wermter (University of Hamburg), Continual Lifelong Learning with Neural Networks
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###IMPORTANT DATES###
* Deadline of full paper submission: October 31, 2018
* Notification of paper acceptance: December 31, 2018
* Camera-ready submission: January 31, 2019
* Early registration deadline: January 15, 2019
* Registration deadline: January 31, 2019
* Conference date: April 16 - 18, 2019
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###SCOPE###
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.
Example topics of interest includes but is not limited to the following:
Big Data Science and Foundations
* Novel Theoretical Models for Big Data
* New Computational Models for Big Data
* Data and Information Quality for Big Data
Big Data Mining
* Social Web Mining
* Data Acquisition, Integration, Cleaning, and Best Practices
* Visualization Analytics for Big Data
* Computational Modeling and Data Integration
* Large-scale Recommendation Systems and Social Media Systems
* Cloud/Grid/StreamData Mining
* Big Velocity Data
* Link and Graph Mining
* Semantic-based Data Mining and Data Preprocessing
* Mobility and Big Data
* Multimedia and Multistructured Data-Big Variety Data
Modern Practical Deep Networks
* Deep Feedforward Networks
* Regularization for Deep Learning
* Optimization for Training Deep Models
* Convolutional Networks
* Sequence Modeling: Recurrent and Recursive Nets
* Practical Methodology
Deep Learning Research
* Linear Factor Models
* Autoencoders
* Representation Learning
* Structured Probabilistic Models for Deep Learning
* Monte Carlo Methods
* Confronting the Partition Function
* Approximate Inference
* Deep Generative Models