The 8^th International Conference on Extreme Learning Machines (ELM2017), 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 Early Bird Registration deadline: August 20, 2017

The main theme of ELM2017 is: Intelligent Things, Smart Chips, Hierarchical Machine Learning and Biological Learning

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.

In addition to the multiple tracks of ELM technical sessions on October 6, 2017 and social networking on October 7, 2017, 27 keynotes will be arranged in ELM2017 on October 4-5, 2017. The keynote speakers include the pioneers of deep learning and random forest, and also researchers / professors from Harvard Medical School, MIT, Nanyang Technological University, IBM Watson, Amazon Web Services, George Institute of Technology, Michigan State University, University of Iowa, University of New Castle, Tsinghua University, Chinese Academy of Science, Shanghai Jiatong University, etc. Some topics are extreme learning machines, deep learning, random forest, brain computer interfaces as well as ELM applications (such as local positioning systems, neuromorphic and memoristor chips, remote sensing, smart grid, robots, etc)

Extreme Learning Machines, Random Forest, and Deep Learning

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 Web Services, USA, "Towards Next Generation of Deep Learning Frameworks"

Xin Yao, University of Birmingham, UK, "Ensemble Approaches to Class Imbalance Learning"

Brain Science, Machine Learning, Brain-Machine Interface, and Healthcare

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

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

Mohammad Ghassemi, Massachusetts Institute of Technology, “Predicting Neurologic Outcome Following Cardiac Arrest”

Gang Pan, Zhejiang University, China, “Brain-Machine Interfaces: Connecting Machine and Biological Intelligence”

Jimeng Sun, George Institute of Technology, USA, “Automated Sleep Study via Deep learning”

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

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"

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 Algorithms, Applications and Smart Chips

Lihua Xie, Nanyang Technological University, Singapore, "Indoor Positioning Systems: Some Recent Development and Challenges"

Fuchun Sun, Tsinghua University, China, “Experience Learning for Robot Dexterous Operations Using ELMs”

Arindam Basu, Nanyang Technological University, Singapore, “Designing ‘Intelligent’ Chips in the Face of Statistical Variations: The Neuromorphic Solution"

Erik Cambria, Nanyang Technological University, Singapore, "Extreme Learning Machines for Commonsense Reasoning and Sentiment Analysis"

Amir Hussain, University of Stirling, UK, "Extreme Learning Machine for Dimensionality Reduction" (tentative)

Lei Zhang, Chongqing University, China, "Advanced Transfer Learning in Intelligent Vision and Olfaction"

Xi-Zhao Wang, Shenzhen University, China, "ELM Tree and Its Spark Implementation"

Amaury Lendasse, University of Iowa, USA, "Applying Machine Learning to Open-Source Learning Management System in order to Develop Visualizations of Students’ Risk of Not Succeeding in STEM Courses"

Kar-Ann Toh, Yonsei University, South Korea, "Deterministic Methods for Pattern Classification"

Chenwei Deng, Beijing Institute of Technology, China, "ELM Feature Learning and Its Applications in Remote Sensing"

Zhaoyang Dong, University of New Castle, Australia, "Machine Learning in Smart Grid"