Abbrevation
INNSBDDL
City
GENOA
Country
Italy
Deadline Paper
Start Date
End Date
Abstract

<div>The 2019 INNS Big Data and Deep Learning (INNSBDDL 2019) conference will be held in Sestri Levante, Italy, April 16 – 18, 2019&#046; 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&#046; 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&#046; Moreover, workshops and tutorials will also be featured&#046;<br></div><div><br></div><div>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&#046; Both theoretical and practical results are welcome&#046;<br>Example topics of interest includes but is not limited to the following:<br>Big Data Science and Foundations<br>* Novel Theoretical Models for Big Data<br>* New Computational Models for Big Data<br>* Data and Information Quality for Big Data<br>Big Data Mining<br>* Social Web Mining<br>* Data Acquisition, Integration, Cleaning, and Best Practices<br>* Visualization Analytics for Big Data<br>* Computational Modeling and Data Integration<br>* Large&#8211;scale Recommendation Systems and Social Media Systems<br>* Cloud/Grid/StreamData Mining<br>* Big Velocity Data<br>* Link and Graph Mining<br>* Semantic&#8211;based Data Mining and Data Preprocessing<br>* Mobility and Big Data<br>* Multimedia and Multistructured Data&#8211;Big Variety Data<br>Modern Practical Deep Networks<br>* Deep Feedforward Networks<br>* Regularization for Deep Learning<br>* Optimization for Training Deep Models<br>* Convolutional Networks<br>* Sequence Modeling: Recurrent and Recursive Nets<br>* Practical Methodology<br>Deep Learning Research<br>* Linear Factor Models<br>* Autoencoders<br>* Representation Learning<br>* Structured Probabilistic Models for Deep Learning<br>* Monte Carlo Methods<br>* Confronting the Partition Function<br>* Approximate Inference<br>* Deep Generative Models<br></div><div><br></div>