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Extreme Learning Machines 2021

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 ELM2021 is: Hierarchical ELM, AI for IoT, Synergy of Machine Learning and Biological Learning.

Call for Papers

The first call for papers is available at THIS LINK.

Accepted papers will be published in conference proceedings (LNCS).

Organized by Arcada University of Applied Sciences, Finland, and co‐organized by University of Houston, Texas, USA, and Mind PointEye, Singapore, ELM2021 will be held online. 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.

Topics of interest

Submissions related to ELM technique are preferred although not compulsory. Topics of interest include but are not limited to:

Theories

  • Sciences of artificial Intelligence, machine learning science and data analytics
  • Biological learning mechanism and neuroscience

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 of deep learning and ELM

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

Hardware

  • Lower power, low latency hardware / chips
  • Artificial biological alike neurons / synapses

Paper submission

All submissions will go through rigorous double review. Details on manuscript submission can be found at Authors.