Extreme Learning Machines (ELM) - Filling the Gap between Frank Rosenblatt's Dream and John von Neumann's Puzzle?

Speaker:        Dr. Guang-Bin Huang
                Nanyang Technological University
                Singapore

Title:          "Extreme Learning Machines (ELM) -
                 Filling the Gap between Frank Rosenblatt's Dream and John
                 von Neumann's Puzzle?"

Date:           Monday, 12 October 2015

Time:           2:00pm - 3:00pm

Venue:          Room 2463 (via lift nos.25/26), HKUST

Abstract:

Artificial intelligence and machine learning have become hot in the past
years. This talk will analyse the reasons behind the reviving of
artificial intelligence and machine learning from both technologies and
industrial demand aspects. This talk will introduce a new generation of
learning theory (the resultant biologically inspired learning technique
referred to as Extreme Learning Machine (ELM)) and its wide type of
applications. ELM outperforms some popular learning methods (e.g., support
vector machines) with faster learning speed (up to thousands times) and
higher accuracies. This talk shows the potential trend of combining ELM
and deep learning (DL), which not only expedites the learning speed (up to
thousands times faster) and reduces the learning complexity but also
improves the learning accuracy in some benchmarking applications and
datasets such as MNIST OCR, traffic sign recognition, hand gesture
recognition, 3D Graphics, etc.  ELM theories can indeed give some
theoretical support to local receptive fields and pooling strategies which
are popularly used in deep learning. ELM theories may have explained the
reasons why the brain are globally ordered but may be locally random.


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Biography:

Guang-Bin Huang serves as an Associate Editor of Neurocomputing, Cognitive
Computation, Neural Networks, and IEEE Transactions on Cybernetics. He was
awarded "Highly Cited Researcher" and listed in "2014 The World's Most
Influential Scientific Minds" by Thomson Reuters. He received the best
paper award from IEEE Transactions on Neural Networks and Learning Systems
(2013). He was invited to give keynotes on numerous international
conferences.

His current research interests include big data analytics, human computer
interface, brain computer interface, image processing/understanding,
machine learning theories and algorithms, extreme learning machine, and
pattern recognition. From May 2001, he has been working as an Assistant
Professor and Associate Professor (with tenure) in the School of
Electrical and Electronic Engineering, Nanyang Technological University,
Singapore. He is Principal Investigator of BMW-NTU Joint Future Mobility
Lab on Human Machine Interface and Assisted Driving, Principal
Investigator (data and video analytics) of Delta - NTU Lab, Principal
Investigator (Scene Understanding) of ST Engineering - NTU Corporate Lab,
and Principal Investigator (Marine Data Analysis and Prediction) of Rolls
Royce - NTU Corporate Lab. He has led/implemented several key industrial
projects (e.g., Chief architect/designer and technical leader of Singapore
Changi Airport Cargo Terminal 5 Inventory Control System (T5 ICS)
Upgrading Project, etc).

One of his main works is to propose a new machine learning theory and
learning techniques called Extreme Learning Machines (ELM), which fills
the gap between traditional feedforward neural networks, support vector
machines, clustering and feature learning techniques. ELM theories have
recently been confirmed with biological learning evidence directly, and
filled the gap between machine learning and biological learning. ELM
theories have also addressed "Father of Computers" J. von Neumann's
concern on why "an imperfect neural network, containing many random
connections, can be made to perform reliably those functions which might
be represented by idealized wiring diagrams."