[Brazil] ELM Special Session in IJCNN2018 / WCCI2018, Brazil

The 2018 IEEE World Congress on Computational Intelligence

2018 International Joint Conference on Neural Networks (IJCNN'2018)

July 8-13, 2018, Rio de Janeiro, Brazil

Special Session on “Extreme Learning Machines (ELM)

 

Over the past few decades, conventional computational intelligence techniques faced bottlenecks in learning (e.g., intensive human intervention and time consuming). With the ever increasing demand of computational power particularly in areas of big data computing, brain science, cognition and reasoning, emergent computational intelligence techniques such as extreme learning machines (ELM) offer significant benefits including fast learning speed, ease of implementation and minimal human intervention.

 

Extreme Learning Machines (ELM) aim to break the barriers between the conventional artificial learning techniques and biological learning mechanism. ELM represents a suite of machine learning techniques for hierarchical neural networks (including but not limited to single and multi- hidden layer feedforward neural networks) in which hidden neurons need not be tuned: inherited from their ancestors or randomly generated. From ELM theories point of view, the entire networks are structured and ordered, but they may be seemingly ‘‘messy’’ and ‘‘unstructured’’ in a particular layer or neuron slice. ‘‘Hard wiring’’ can be randomly built locally with full connection or partial connections. Coexistence of globally structured architectures and locally random hidden neurons happen to have fundamental learning capabilities of compression, sparse coding, feature learning, clustering, regression and classification. ELM theories also give theoretical support to local receptive fields in visual systems.

 

ELM learning theories show that hidden neurons (including biological neurons whose math modelling may be unknown) (with almost any nonlinear piecewise activation functions) can be randomly generated independent of training data and application environments, which has recently been confirmed with concrete biological evidences. ELM theories and algorithms argue that “random hidden neurons” capture the essence of some brain learning mechanism as well as the intuitive sense that the efficiency of brain learning need not rely on computing power of neurons. This may somehow hint at possible reasons why the brain is more intelligent and effective than computers. ELM offers significant advantages such as fast learning speed, ease of implementation, and minimal human intervention. ELM has good potential as a viable alternative technique for large-scale computing and artificial intelligence.

 

The need for efficient and fast computational techniques poses many research challenges. This special session seeks to promote novel research investigations in ELM and related areas.

 

Topics of interest:

 

All the original papers related to ELM technique are welcome.  Topics of interest include but are not limited to:

 

Theories

        Universal approximation, classification and convergence, robustness and stability analysis

         Biological learning mechanism and neuroscience

         Machine learning science and data science

Algorithms

         Real-time learning, reasoning and cognition

         Sequential/incremental learning and kernel learning

         Clustering and feature extraction/selection/learning

         Random projection, dimensionality reduction, and matrix factorization

         Closed form and non-closed form solutions

         Hierarchical solutions, and combination of deep learning and ELM

         No-Prop, Random Kitchen Sink, FastFood, QuickNet, RVFL, Echo State Networks

         Parallel and distributed computing / cloud computing

Applications

         Time series prediction, smart grid and financial data analysis

         Social media and video applications

         Biometrics and bioinformatics, security and compression

         Human computer interface and brain computer interface

         Cognitive science/computation

         Sentic computing, natural language processing and speech processing

         Big data analytics

Hardware

         Lower power, low latency hardware / chips

         Artificial biological alike neurons / synapses

 

Paper submission:

Potential authors may submit their manuscripts for presentation consideration through WCCI2018 submission system. All the submissions will go through peer review. Details on manuscript submission can be found from http://www.ecomp.poli.br/~wcci2018/submissions/  

 

Important dates:

Paper submission deadline:                                                               January 15, 2018           

Notification of acceptance:                                                                March 15, 2018

Final paper submission and early registration deadline:                 May 1, 2018     

 

Organizers:

Guang-Bin Huang, Nanyang Technological University, egbhuang@ntu.edu.sg

Bao-Liang Lu, Shanghao Jiaotong University, China, bllu@sjtu.edu.cn

Jonathan Wu, University of Windsor, Canada, jwu@uwindsor.ca

Donald C. Wunsch II, Missouri University of Science & Technology, USA, dwunsch@mst.edu 

 

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