APNNS Advanced Forum Series: Deep Learningand Artificial Intelligence Winter School 2018
1st – 4th February 2018, Bangkok, Thailand

ABSTRACTProfessor Irwin King
Associate Dean (Education), Engineering Faculty
Director, Rich Media Big Data Analytics and Application KeyLaboratory, Shenzhen Research Institute (SZRI), The Chinese
University of Hong Kong
Principal Investigator, The Knowledge and Education Exchange
Platform (KEEP) Project, SMIEEE (CIS), MACM, SMINNS (VP,BoG), APNNA (VP, BoG), FHKIE, IrwinKing.com
Topic: Online Learning for Big Data Applications
ABSTRACT. Online learning investigates sequential decisions with uncertainty, in which learning models
generally are updated without reusing training samples. As data generated from sciences, business,governments, etc. are reaching petabyte or even Exabyte, and perform other characteristics (such as nonstationarity and imbalance), theories, models, and applications in online learning are becoming importantin machine learning to process a large amount of streaming data effectively and efficiently. Recently, anumber of online learning algorithms have been proposed to tackle sequential decisions with uncertainty,
especially for cases of big data volume, non-stationary and/or highly imbalanced data. In this talk, we focus
on some new developments of online learning technologies in both theory and applications. Relevant topics
including Multi-Armed Bandits (MAB), online learning in stochastic settings, online learning with
contextual information, and unsupervised online hashing, will be discussed. Moreover, some of our recent
works such as combinatorial exploration of MAB, locality-sensitive linear bandits, online learning withimbalanced data, and faster online hashing, will also be presented to demonstrate how online learning
approaches can be effectively applied to big data.

Topic: Randomized Algorithms for Efficient Learning
ABSTRACT. As data routinely collected from social science, business, education, biology, etc. arereaching petabyte or even exabyte at an alarming rate, developing approaches for enabling learning with
big data have been critically important. Recently, randomized algorithms stemming from numerical linear
algebra have attracted much attention, and these randomized approaches may produce approximate
solutions to certain large problems very efficiently. In this talk, we offer a high-level introduction torandomized algorithms and a set of associated applications including kernel learning, regression, clustering,
deep learning, etc. Moreover, some of our recent work such as randomized algorithms for covariance matrix
estimation and unsupervised online hashing, will also be presented to demonstrate how randomized
algorithms can effectively handle large-scale machine learning with big data.

Professor Nikola Kasabov
FIEEE, FRSNZ, DVF RAE UK, Director, Knowledge Engineeringand Discovery Research Institute, Auckland University of Technology,
Auckland, New Zealand

Topic: Brain-Inspired Artificial Intelligence and Deep
Learning with Spiking Neural Networks: Methods, Systems,
Applications
ABSTRACT. Brain-inspired AI (BI-AI) is the contemporary phase in the AI development that isconcerned with the design and implementation of highly intelligent machines that utilise information
processing principles from the human brain, along with their applications. Spiking neural networks(SNN) and deep learning algorithms make it possible for the BI-AI to gain a fast progress nowadays.
This presentation has two parts. The first part covers generic methodological aspects, including:Learning and understanding evolving processes in space and time. Data, Information andKnowledge; The human brain as a deep learning, evolving system; A Brief history of AI; Methodsof Spiking Neural Networks (SNN); Brain-inspired SNN architectures; Evolutionary and quantuminspired optimisation of SNN.
The second part presents specific methods, systems and applications based on deep learning in SNNand BI-AI for various applications, including: Deep learning of audio/visual data; Deep learning ofbrain EEG data; Deep learning of fMRI data; Brain-Computer Interfaces (BCI) and human-machine
symbiosis; Computational Neurogenetic Modelling in Bio/Neuroinformatics; Personalisedmodelling in Bio/Neuroinformatics; Multisensory predictive modelling of ecological andenvironmental data; Neuromorphic computer systems. It concludes with discussions and future
directions.
A brain –inspired SNN architecture NeuCube is used as illustration. Development software andapplication systems based on NeuCube can be found on: http://www.kedri.aut.ac.nz/neucube/

Professor Simon Fong
Associate Professor at Department of Computer and Information
Science and Data Analytics and Collaborative Computing Laboratory,
University of Macau, Taipa, Macau SAR
Topic: Optimizing Hyper-parameters for Deep Learning Using
Meta-Heuristic Algorithms
ABSTRACT. Deep learning (DL) is one of the most emerging types of contemporary machine learningtechniques that mimic the cognitive patterns of animal visual cortex to learn the new abstract features
automatically by deep and hierarchical layers. Hyper-parameter optimization is a model selection problem
that attempts to find the best solution among a combination set of parameters with an aim to attain highaccuracy. However, the trial and error methods to optimize the hyper-parameters take a very long time totest the accuracy given that there are many variables and some of their numerical data types are continuous.
In this talk, we discuss an architecture that can be used for optimizing the hyper-parameters of neuralnetwork using metaheuristics. The comparison results of using meta-heuristic methods and traditional
exhaustive search method would be presented. The novelty of applying the swarm search methods forfinding the best hyper-parameters would be demonstrated. The pros and cons of swarm search methods for
hyper-parameter optimization would be discussed, as well as pointing out some future directions of thehyper-parameters optimization.

Professor Soo-Young Lee
Institute for Artificial Intelligence & Brain Science Research Center
Korea Advanced Institute of Science and Technology
Topic: Deep Learning and Human Audio-Visual Pathways
ABSTRACT. Recently deep learning had attracted a lot of attention from bothacademic and industrial communities for image and speech recognition tasks. However, the basic models
are not new and have strong connection to the audio and visual processing models in our brain. This Lecture
will bring the connection between deep neural networks and cognitive computational models for audiovisual information processing. We will first discuss the cognitive scientific facts on information processing
mechanism in human audio and visual pathways, and then move to the computational models in the formof deep learning for the hierarchical feature extraction, stereo/binaural spatial information processing,selective attention, and audio-visual integration.

Topic: Deep Learning for Emotion and Top-down Attention
ABSTRACT. Human emotion is an internal state of human brain which makes different decision andbehavior from same sensory inputs. Therefore, for efficient interactions between human and machine, i.e.
chatbot, it is important for the machine to estimate human emotions. Due to the internal nature, theclassification accuracy of the emotion from a single modality is not high. For example, our result was ranked
as Top-1 with only 61.6% accuracy for the emotion recognition task from facial images at EmotiW2015
challenge. In this tutorial we will first present a hierarchical Committee machine to win EmotiW2015
challenge, and further extend the ideas to multi-modal classification with top-down attention andidentification of brain signals for the two-other brain internal states.

Topic: Understanding Brain Internal States
ABSTRACT. At the third Lecture we will introduce cognitive neuroscience of brain internal states, akaMind, which may consist of emotion, personality, intention, etc. The emotion is relatively better studied incognitive neuroscience and probably the only one with engineering applications at this moment. We will
introduce two cognitive neuroscientific findings on the other brain internal states. Especially, we madehypothesis on 2 axes of the internal state space, i.e., agreement/disagreement and trust/distrust toconversational counterparts during conversation and identified fMRI and/or EEG signal components related
to those internal states. Then, we will propose a method to utilize these brain signals for generating nearground-truth labels of brain internal states, which will be used to train classifiers from audio-visual signals.

Professor Sung-Bae Cho
Professor at Department of Computer Science, Yonsei University
Topic: Hybrid Deep Learning for Anomaly Detection
ABSTRACT. In the field of deep learning, a generative model via an adversarial
process gets a great attention due to the amazing demonstration of performance.
It can simultaneously train a generative model to capture the data distribution, and discriminative model toestimate the probability that a sample came from the training data. In this talk, I will present a new methodof transfer-generative adversarial network (tGAN) with auto-encoders to detect anomaly in malicious
software (malware) for computer security. The proposed method of malware detection treats zero-day
attacks by generating fake malware and learning to distinguish it from real malware. The data generated
from a random distribution are similar but not identical to the real data: it includes modified featurescompared with real data. The detector learns various malware features using real data and modified data
generated by the tGAN based on a deep auto-encoder (DAE), which stabilizes the GAN training. Before
training the GAN, the DAE learns malware characteristics, produces general data, and transfers thiscapacity for stable training of the GAN generator. The trained discriminator passes down the ability tocapture malware features to the detector, using transfer learning.

Topic: Prospects and Challenges of Deep Learning
ABSTRACT. Recently, deep learning opens another renaissance of artificial intelligence that is a long
dream of human-beings. There are four representative models for deep learning, and two of them can beconvolutional neural networks and recurrent neural networks. In this talk, I will give the general idea ofboth methods, and discuss about the prospects and challenges. Especially, to work out realistic problems,
we need a hybrid architecture of several deep learning models. I will also present a generative model via anadversarial process gets a great attention due to the amazing demonstration of performance. It cansimultaneously train a generative model to capture the data distribution, and discriminative model toestimate the probability that a sample came from the training data. Experiments with the malware dataset
from the Kaggle Microsoft malware classification challenge (https://www.kaggle.com/c/malwareclassification) show that the tGAN achieves 95.74% average classification accuracy which is higher thanaccuracy of other state-of-the-art methods and increases the learning stability.

Professor Weng-Kin Lai
Associate Professor at Department of Electrical & Electronic
Engineering, Tunku Abdul Rahman University College
Topic: A machine intelligence Approach to Identify the Quality ofNatural Rubber Latex
ABSTRACT. Natural rubber latex is readily obtained from many parts of South East Asia and as arenewable resource. It is widely used to make many common household and industrial products.
Unfortunately, as its quality is not consistent due to a variety of reasons there is a need to be able to measureits quality. A common measure of its quality is the mechanical stability, which is defined as the time at thefirst onset of flocculation when the latex is subjected to physical stress. Currently, the assessment isperformed manually by trained personnel, closely adhering to the specifications defined by the ISO35
standard mechanical stability test that is widely adopted by the rubber industry. Nevertheless, there is somelevel of subjectivity involved as the test heavily depends on the human eyesight as well as the technician’sexperience. In this presentation we will share some of the results from a machine intelligence approach todetermine the quality of natural rubber latex. Experimental results demonstrated that the proposed approach
was able to provide good accuracies in identifying the quality of natural rubber latex.