ELM2018 1st CFP: The 9th International Conference on Extreme Learning Machines (ELM2018)

 

The 9th International Conference on Extreme Learning Machines (ELM2018)

Marina Bay Sands, Singapore, November 21 – 23, 2018

 

Organized by: Nanyang Technological University, Singapore

Co-organized by: Tsinghua University, China; Shanghai Jiaotong University, China; University of New South Wales, Australia; City University of Hong Kong

 

Manuscript Submission Link: http://elm2018.extreme-learning-machines.org (now open)

 

Extreme Learning Machines (ELM) aims to enable pervasive learning and pervasive intelligence. As advocated by ELM theories, it is exciting to see the convergence of machine learning and biological learning from the long-term point of view. ELM may be one of the fundamental `learning particles’ filling the gaps between machine learning and biological learning (of which activation functions are even unknown). ELM  represents a suite of (machine and biological) learning techniques in which hidden neurons need not be tuned: inherited from their ancestors or randomly generated. ELM learning theories show that effective learning algorithms can be derived based on randomly generated hidden neurons (biological neurons, artificial neurons, wavelets, Fourier series, etc) as long as they are nonlinear piecewise continuous, independent of training data and application environments. Increasingly, evidence from neuroscience suggests that similar principles apply in biological learning systems. ELM theories and algorithms argue that “random hidden neurons” capture an essential aspect of biological learning mechanisms as well as the intuitive sense that the efficiency of biological learning need not rely on computing power of neurons. ELM theories thus hint at possible reasons why the brain is more intelligent and effective than current computers.

 

The main theme of ELM2018 is: Hierarchical ELM, AI for IoT, Synergy of Machine Learning and Biological Learning

 

Organized by Nanyang Technological University, Singapore, and co‐organized by Tsinghua University, Shanghai Jiaotong University, China, University of New South Wales, Australia and City University of Hong Kong, ELM2018 will be held in Singapore. This conference will provide a forum for academics, researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the ELM technique and biological learning.

 

Tutorial proposals:

All interesting topics on general artificial intelligence and machine learning techniques are welcome, which include but not limited to: deep learning, hierarchical learning, reinforcement learning, sparse coding, clustering, extreme learning machines, etc.

 

Accepted papers presented in this conference will be published in conference proceedings and selected papers will be recommended to reputable ISI indexed international journals.

 

Topics of interest:

All the submissions must be related to ELM technique.  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

         Parallel and distributed computing / cloud computing

Applications

         AI in IoT (Internet of Things)

         Financial data analysis

         Smart grid and renewable energy systems

         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:

All submissions will go through rigorous peer review. Details on manuscript submission will be given online http://elm2018.extreme-learning-machines.org by April 20, 2018.

 

Important dates:

Paper submission deadline:    July 1, 2018

Notification of acceptance:     August 1, 2018

Registration deadline:              September 1, 2018

 

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