Call for Papers

The 2019 INNS Big Data and Deep Learning (INNSBDDL 2019) conference will be held in Sestri Levante, Italy, April 16–18, 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.

Important Dates

    • Deadline of full paper submission: October 31, 2018 December 7, 2018
    • Notification of paper acceptance: December 31, 2018 January 4, 2019
    • Camera-ready submission: January 31, 2019
    • Early registration deadline: January 15, 2019 January 31, 2019
    • Registration deadline: January 31, 2019 March 1, 2019
    • Conference date: April 16-18, 2019

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 Pre-processing
    • Mobility and Big Data
    • Multimedia and Multi-structured 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

Works submitted as a regular paper will be published in a serie indexed by Scopus. Submitted papers will be reviewed by some PC members based on technical quality, relevance, originality, significance and clarity. At least one author of an accepted submission should register to present their work at the conference. Selected papers presented at INNS BDDL 2019 will be included in special issues of top journals in the field (prospected journals: Big Data Research, Transaction on Neural Networks and Learning System, Neurocomputing, etc).