posted by organizer: claudiogalicchio || 2216 views || tracked by 5 users: [display]

Randomized ML @ ESANN 2017 : Randomized Machine Learning approaches: analysis and developments

FacebookTwitterLinkedInGoogle

Link: https://www.elen.ucl.ac.be/esann/index.php?pg=specsess#random
 
When Apr 26, 2017 - Apr 28, 2017
Where Bruges, Belgium
Submission Deadline Nov 26, 2016
Notification Due Jan 31, 2017
Categories    machine learning   neural networks   deep learning   reservoir computing
 

Call For Papers

-------------------------------------

Scope and Topics

-------------------------------------

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

-------------------------------------

* Paper submission deadline (extended): 26 November 2016

* Notification of acceptance: 31 January 2017

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


Paper Submission

-------------------------------------

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

-------------------------------------

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).

Related Resources

ICML 2017   34th International Conference on Machine Learning
IJCAI 2017   International Joint Conference on Artificial Intelligence
ECML-PKDD 2017   European Conference on Machine Learning and Principles and Practice of Knowledge Discovery
DSAA 2017   The 4th IEEE International Conference on Data Science and Advanced Analytics 2017
KDD 2017   Knowledge Discovery and Data Mining
ICONIP 2017   International Conference on Neural Information Processing
ICANN 2017   International Conference on Artificial Neural Networks 2017
MLDM 2017   Machine Learning and Data Mining in Pattern Recognition
NLDB 2017   22nd International Conference on Natural Language & Information Systems
Infovis & ML CFP at ESANN 2016   Special session on Information Visualisation and Machine Learning: Techniques, Validation and Integration at ESANN 2016