Keynote and Invited Speakers

1.

Ben Goertzel

CEO of SingularityNET, AGI Conference Chair     

Email: ben@goertzel.org

 

Abstract:

 

 

 

Biography



2. Tensor networks and their applications in image and time series reconstruction and prediction

Andrzej Cichocki 

SKOLTECH, Moscow      

Email: a.cichocki@riken.jp

 

Abstract: Tensor factorizations / decompositions  and their extensions tensor networks  have become  emerging  techniques in AI and  machine learning methods. Tensor networks  have been already successfully  used over a number   of applied areas in machine learning, including  deep neural networks,  generalized regression,   classification,  clustering,  support tensor machines (STM), tensor  canonical correlation analysis (TCCA), higher order partial least squares (HOPLS),  multilinear independent component analysis   (MLICA),  Non-negative Tensor Factorization (NTF), multiway robust PCA (MRPCA) and Higher order SVD.

In this talk we briefly overview these applications, and special emphasis will be give to   emerging applications in  image reconstructions and enhancement,  and higher order time series forecasting (TSF).

 

 

Biography

Andrzej Cichocki received the M.Sc. (with honors), Ph.D. and Dr.Sc. (Habilitation) degrees, all in electrical engineering from Warsaw University of Technology (Poland). He spent several years at University Erlangen (Germany) as an Alexander-von-Humboldt Research Fellow and Guest Professor. He was a Senior Team Leader and Head of the laboratory for Advanced Brain Signal Processing, at RIKEN Brain Science Institute (Japan) and now he is a Professor in the Skolkovo Institute of Science and Technology - SKOLTECH (Russia). He is author of more than 500 technical papers and 5 monographs in English (3 of them translated to Chinese). He served as Associated Editor of, IEEE Trans. on Signals Processing, IEEE Trans. on Neural Networks and Learning Systems, IEEE Trans on Cybernetics,  Journal of Neuroscience Methods and he was as founding Editor in Chief for Journal Computational Intelligence and Neuroscience. Currently, his research focus on deep learning, multiway blind source separation, tensor decompositions, tensor networks for big data analytics, and Brain Computer Interface. His publications currently report over 41,000 citations according to Google Scholar, with an h-index of 91. He is Fellow of the IEEE since 2013.

 

3. Behavior based Artificial Intelligence from the perspective of automatic control

Fuchun SUN 

Department of Computer Science and Technology, Tsinghua University      

Email: fcsun@tsinghua.edu.cn

 

 

Abstract: In the development of human society, the invention and use of machine is an important sign of human being's entering into industrialization, and machine becomes an independent actuator, which realizes the separation of executor and action instruction sender from our human. Furthermore, the emergence of artificial intelligence makes it possible for a machine to become a sender of instructions through interactions with environment and human, and as a result, the sender and executor of the instruction are unified on the machine itself. This report systematically analyzes behavior-based artificial intelligence from evolution of automatic control. Then, taking "how to sense like a human being" as the topic, the talk puts forward the framework of robot active perception, introduces the main achievements of the team in visual multi-target detection, visual tactile representation, multimodal fusion and developmental learning, and takes "how to operate like a human being" as the problem, gives the main achievements of the team in learning smart operation skills such as active imitation learning and preference learning achievements. Next, taking "how to make decision like a human being" as the topic, some new points are given in terms of micro-size intelligence. Finally, the development trend of robot dexterous operation skill learning under the condition of incomplete information is given.

 

 

Biography

Dr. Fuchun Sun is professor of Department of Computer Science and Technology and President of Academic Committee of the Department, Tsinghua University, deputy director of State Key Lab. of Intelligent Technology & Systems, Beijing, China. He also serve as Vice president of Society of Chinese Association for Artificial Intelligence and executive director of China Automation Society. His research interests include robotic perception and intelligent operations. He has won the Champion of Autonoumous Grasp Challenges in IROS2016 and IROS 2019.
Dr. Sun is the recipient of the excellent Doctoral Dissertation Prize of China in 2000 by MOE of China and the Choon-Gang Academic Award by Korea in 2003, and was recognized as a Distinguished Young Scholar in 2006 by the Natural Science Foundation of China. He served as associated editors of IEEE Trans. on Neural Networks during 2006-2010, IEEE Trans. On Fuzzy Systems during 2011-, IEEE Trans. on Cognitive and Development Systems since 2018 and IEEE Trans. on Systems, Man and Cybernetics: Systems since 2015.


4. Tensor Network Representations in Machine Learning

Qibin Zhao

RIKEN AIP, Japan

Email: qibin.zhao@riken.jp

 

 

 

Abstract:

Tensor networks are factorizations of very large tensors into networks of smaller tensors, it is shown to be a general extension of typical tensor decomposition to high dimensional case. Recently, tensor networks are also increasingly finding applications in machine learning such as model compression or acceleration of computations. In this talk, I will firstly present the general concept of tensor network related research in machine learning, and then introduce our studies on fundamental tensor network model, algorithm, and applications. In particular, the tensor ring decomposition model is introduced and shown to be powerful and efficient representations. In addition, we will present recent progresses on how tensor networks can be employed to solve challenging problems in tensor completion, multi-task learning and multi-modal learning.

 

 

Biography

Qibin Zhao received the Ph.D. degree in computer science from Shanghai Jiao Tong University, China in 2009. He was a research scientist at RIKEN Brain Science Institute from 2009 to 2017. He is currently a unit leader for tensor learning unit at RIKEN Center for Advanced Intelligence Project (AIP). His research interests include machine learning, tensor factorization and tensor networks, computer vision and brain signal processing. He has published more than 100 papers in international journals and conferences and two monographs. He is a senior member of IEEE and serve as an editorial board member for Science China Technological Sciences.

5.  Uncertainty between prediction and interpretability

Junping Zhang

Shanghai Key Laboratory of Intelligent Information Processing
Fudan University200433, China
Email: jpzhang@fudan.edu.cn

 

 

 

Abstract:

In this topic, I will briefly survey the development of deep learning, and try to uncover a miracle that deep learning have superior prediction performance. Then I would like to talk about the uncertatinty between prediction and interpretability, and some potential research direction from the angle of uncertainty.

Biography

Junping Zhang is a professor at School of Computer Science, Fudan University since 2011. His research interests include machine learning, image processing, biometric authentication, and intelligent transportation systems. He has been an associate editor of the IEEE INTELLIGENT SYSTEMS since 2009 and the IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS from 2010 to 2018. He has widely published in high-impact international journals and conferences such as TPAMI, TNNLS, TIP, ToC, TAC and ICML, AAAI, ECCV, ACM MM.

6.  Neuromorphic Computing Approach to Auditory and Visual Perception

Huajin Tang

College of Computer Science and Technology
Zhejiang University310058, China
Email: htang@zju.edu.cn

 

 

 

Abstract:

 In recent years neuromorphic computing has become an important methodology to emulate brain style intelligence. There has been rapid progress in computational theory, learning algorithms, signal processing and circuit design and implementation. By using neural spikes to represent the outputs of sensors and for communication between computing blocks, and using spike timing based learning algorithms, neuromorphic computational models and hardware have achieved promising real-time learning performance. This talk will start from introducing the computational principles and architecture found in neural systems, and present a new theme of neuromorphic approach to auditory and visual perception.

Biography

Prof. Huajin Tang received the B.Eng. degree from Zhejiang University, China in 1998, received the M.Eng. degree from Shanghai Jiao Tong University, China in 2001, and received the Ph.D. degree from the National University of Singapore, in 2005. Prof. Tang’s research interests include neuromorphic computing, neuromorphic hardware and cognitive systems, robotic cognition, etc. He received 2016 IEEE Transactions on Neural Networks and Learning Systems Outstanding Paper Award, 2019 IEEE Computational Intelligence Magazine Outstanding Paper Award. Dr. Tang has served as an Associate Editor of IEEE Trans. on Neural Networks and Learning Systems, IEEE Trans. on Cognitive and Developmental Systems and Frontiers in Neuromorphic Engineering. He was the Program Chair of IEEE CIS-RAM (2015, 2017), International Symposium on Neural Networks (2019) and Chair of IEEE Symposium on Neuromorphic Cognitive Computing. and. He is now a member of the Board of Governors for International Neural Network Society.

7. Neuromusicology with machine learning – how human understands music

Toshihisa Tanaka

Electrical and Electronic Engineering
Tokyo University of Agriculture and Technology,Japan
Email: tanakat@cc.tuat.ac.jp

 

 

 

Abstract:

This talk addresses my recent neurophysiological studies of music cognition. I will talk about the neural entrainment to the familiarity of a listener with music using both the electroencephalogram (EEG) signals and the music. It is shown that the cross-correlation between EEG and the music when listening to unfamiliar music is significantly stronger than that when listening to famil-iar music. Moreover, the familiarity of music can be classified by machine learning techniques such as neural networks and support vector machines.
 

Biography

Toshihisa Tanaka received the B.E., the M.E., and the Ph.D. degrees from the Tokyo Institute of Technology in 1997, 2000, and 2002, respectively. From 2000 to 2002, he was a JSPS Research Fellow. From October 2002 to March 2004, he was a Research Scientist at RIKEN Brain Science Institute. In April 2004, he joined Department of Electrical and Electronic Engineering, the Tokyo University of Agriculture and Technology, where he is currently a Professor. In 2005, he was a Royal Society visiting fellow at the Communications and Signal Processing Group, Imperial College London, U.K. From June 2011 to October 2011, he was a visiting faculty member in Department of Electrical Engineering, the University of Hawaii at Manoa.

His research interests include a broad area of signal processing and machine learning including brain and biomedical signal processing, brain-machine interfaces and adaptive systems. He is a co-editor of Signal Processing Techniques for Knowledge Extraction and Information Fusion (with Mandic, Springer), 2008 and a leading co-editor of Signal Processing and Machine Learning for Brain-Machine Interfaces (with Arvaneh, IET, UK), 2018.

He served as an associate editor and a guest editor of special issues in journals including Neurocomputing and IEICE Transactions on Fundamentals and Computational Intelligence and Neuroscience (Hindawi). Currently he serves as an associate editor of IEEE Transactions on Neural Networks and Learning Systems, Applied Sciences (MDPI), and Advances in Data Science and Adaptive Analysis (World Scientific). Furthermore, he serves as a member-at-large, board of governors (BoG) of Asia-Pacific Signal and Information Processing Association (APSIPA). He was a chair of the Technical Committee on Biomedical Signal Processing, APSIPA. He is a senior member of IEEE, and a member of IEICE, APSIPA, Japan Epilepsy Society, and Society for Neuroscience.
 

8.  Intelligent Analysis of Brain Imaging for Early Diagnosis of Brain Diseases

                                        

Daoqiang Zhang

Computer Science and Technology  Department,     Nanjing University Aeronautics and  Astronautics, China

Email: dqzhang@nuaa.edu.cn

 

Abstract:

 In recent years, the brain research projects have received considerable public and governmental attentions worldwide. Brain imaging technique is an im-portant tool for brain science research. However, due to the high-dimensional, multi-modality, heterogenous, and time-variant characteristics of brain images, it is very challenging to develop both efficient and effective methods for brain image analysis. In this talk, I will introduce our recent works on intelligent methods of brain imaging, based on machine learning techniques. Specifically, this talk will cover the topics including multi-modal brain image fusion and classification, image genomic association analysis, functional alignment and brain network analysis, as well as their applications in early diagnosis of brain disease and brain decoding. 

Biography

Daoqiang Zhang received the B.S. and Ph.D. degrees in computer science from the Nanjing University of Aeronautics and Astronautics (NUAA), China, in 1999 and 2004, respectively. He joined the Department of Computer Science and Engineering, NUAA, as a Lecturer in 2004, where he is currently a Professor. His research interests include machine learning, pattern recognition, data mining, and medical image analysis. In these areas, he has published over 150 scientific articles in refereed international journals, such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing, IEEE Transactions on Medical Imaging, Pattern Recognition, Neuroimage, Human Brain Mapping, Medical Image Analysis, and conference proceedings, such as IJCAI, AAAI, NIPS, CVPR, KDD, MICCAI with over 10000 citations by the Google Scholar. He was nominated for the National Excellent Doctoral Dissertation Award of China in 2006, received the Most Cited Article Award of Pattern Recognition and Most Cited Paper Award of Neuroimage, received the best paper award or the best student award of several international conferences, such as the PRICAI’06, STMI’12, and BICS’16. He has served as a Program Committee Member for several international and native conferences, such as the IJCAI, AAAI, NIPS, MICCAI, SDM, PRICAI, and ACML.

 

 

9.  Data-Driven Security Analysis of Machine Learning Sytems

 

Chao Shen

School of Electronic and Information Engineering
Xi’an Jiaotong University, China
Email:
cshen@sei.xjtu.edu.cn
 

 

 

Abstract:

Human society is witnessing a wave of machine learning (ML) driven by deep learning techniques, bringing a technological revolution for human production and life. In some specific fields, ML has achieved or even surpassed human-level performance. However, most previous machine learning theories have not considered the open and even adversarial environments, and the security and privacy issues are gradually rising. Besides of insecure code implementations, biased models, adversarial examples, sensor spoofing can also lead to security risks, which are hard to be discovered by traditional security analysis tools. This talk reviews previous works on ML system security and privacy, revealing potential security and privacy risks. Firstly, we introduce a threat model of ML systems, including attack surfaces, attack capabilities and attack goals. Second, we analyze security risks and countermeasures in terms of four critical components in ML systems: data input (sensor), data preprocessing, machine learning model and output. Finally, we discuss future research trends on the security of ML systems. The aim is to arise the attention of the computer security society and the ML society on security and privacy of ML systems, and so that they can work together to unlock ML’s potential to build a bright future.

Biography

Chao Shen is a Professor in School of Electronic and Information Engineering, and serves as the Associate Dean of School of Cyber Science and Engineering, at Xi’an Jiaotong University. He is also a member of Ministry of Education Key Lab for Intelligent Networks and Network Security. He was a research scholar in Computer Science Department of Carnegie Mellon University from 2011 to 2013. He has published over 70 papers in prestigious journals and conferences of cyber security and artificial intelligence fields such as USENIX Security, ACM CCS, IEEE TDSC, IEEE TIFS, Automatica, IEEE TNNLS, and ACM TKDD. He was the recipient of NSFC Excellent Young Scientists Fund. He serves as an associate editor of several international journals, as well as organization committee member or program committee member of several academic conferences.

 

 



Key Laboratory of Intelligent Information Processing
Institute of Computing Technology, Chinese Academy of Sciences