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Scope and Topics

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Randomness has always been present in one or other form in Machine Learning (ML) models; for instance, data sets have been randomly split into two training and test sets; also, random initializations of the parameters have always been common, and even advisable. However, the last few years have observed a change of paradigm, in which randomness is not only accessory, but plays a key role in many occasions, e.g., in the well-known random forests. In the Neural Network (NN) area, since its origins, randomness gave rise to a rich set of models, which have been recently exploited especially for efficiency aims. However, the bias induced by the use NN with random weights deserves further analysis, especially in the novel advances in the fields of deep NN, dynamical systems (Recurrent NN), and NN for learning in structured domains.

This session calls for high level contributions dealing with new analyses and developments of randomized approaches for ML, as a way of enhancing their understanding and performance. The session is also open to critical analysis of randomized approaches and to works that point out potential flaws and limitations of randomized machine learning models.

The topics of the session include, but are not limited, to the following:

Neural Networks with random weights
Extreme Learning Machines, Random Vector Functional-Link Networks
Reservoir Computing
Deep Randomized Neural Networks
Random learning algorithms
Random ensembles: random forests, extremely randomized trees, random combinations of neural networks, etc.
Novel randomized models for Structured Data (sequences, trees, graphs)
Random Projections
Randomized search of optimal parameters
Efficient design of random models for Big Data
Theory of Randomized Neural Networks
Open issues and limitations: learnability, range of applicability, stability and efficiency, comparisons
Biological plausibility/inspiration of Randomized Neural Networks
Parallel Computing for Randomized models
Linear Basis Expansion and Kernel approaches
Bayesian approaches
Development of new ML models using random structures
Performance assessment
Applications

Important Dates

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* Paper submission deadline (extended): 26 November 2016

* Notification of acceptance: 31 January 2017

* ESANN conference: Bruges, Belgium, 26-28 April 2017


Paper Submission

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Papers submitted to this Special Session are reviewed according to the same rules as the submissions to the regular sessions of ESANN 2017. Authors who submit papers to this session are invited to mention it on the author submission form. Submissions to regular and special sessions follow identical format, instructions, deadlines and procedures.

Please find more info at the ESANN 2017 website https://www.elen.ucl.ac.be/esann/



Special Session Organizers

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Claudio Gallicchio (University of Pisa, Italy),

José D. Martín-Guerrero (University of Valencia, Spain),

Alessio Micheli (University of Pisa, Italy),

Emilio Soria (University of Valencia, Spain).