The 8^th International Conference on Extreme Learning Machines (ELM2017)

Dongshan Hotel, Yantai, China, October 4 – 7, 2017

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, China

Registration Link: http://elm2017.extreme-learning-machines.org  Registration deadline: August 15, 2017

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 ELM2017 is: Intelligent Things, Hierarchical Machine Learning and Biological Learning

In addition to the multiple tracks of ELM technical sessions on October 6, 2017, some keynotes are arranged in ELM2017 on October 4-5, 2017 as follows:

Extreme Learning Machines, Random Forest, Deep Learning and Evolutionary Computation

Guang-Bin Huang, Nanyang Technological University, Singapore, "Pervasive Intelligence and Cloud Intelligence Enabling Intelligent Revolution and Intelligent Economy"

Kunihiko Fukushima, Fuzzy Logic Systems Institute, Japan, "Artificial Vision by Deep CNN Neocognitron"

Tin Kam Ho, IBM Watson, USA, "Learning with Random Guesses in Random Decision Forests"

Mu Li, Amazon, USA, "Deep Learning Open Source Software Library: MXNet" (tentative)

Xin Yao, University of Birmingham, UK, "Evolutionary Computation" (tentative title)

Brain Science, Machine Learning and Healthcare

Syd Cash, Harvard Medical School, "Brain Computer Interfaces and Closed Loop Control of Seizures" (tentative)

Jing Jin, Harvard Medical School, "Spike and Seizure Detection"

Haoqi Sun, Harvard Medical School, "Brain Age and Sleep"

Alice Lam, Harvard Medical School, "Detection of Occult Seizures in Dementia"

M. Brandon Westover, Harvard Medical School, "Big Data in Neurology"

Juyang Weng, Michigan State University, USA, "Turing Machine Logic in Brain-Inspired Networks for Vision, Speech, and Natural Languages"

Bao-Liang Lu, Shanghai Jiaotong University, China, "Multimodal Emotion Recognition and Vigilance Estimation with Machine Learning"

Yiqiang Chen, Chinese Academy of Science, China, "Cognition Behaviour Opportunity Learning for Healthcare"

ELM Applications