Prof. Nick Freris
Senior Member of ACM and IEEE
University of Science and Technology of China, China
Speech Title: Adaptive Compression of Deep Neural Networks
Abstract:Model compression is crucial for accelerating deep neural networks while maintaining high prediction accuracy. In this talk, I will present a lightweight compression method termed Adaptive SensiTivity-basEd pRuning (ASTER) which dynamically adjusts the filter pruning threshold
concurrently with the training process. This is accomplished by computing the sensitivity of the loss to the threshold on the fly (without re-training),
as carried with minimal overhead on the Batch Normalization (BN) layers. ASTER then proceeds to adapt the threshold so as to maintain a fine
balance between pruning ratio and model accuracy. Extensive experiments on numerous neural networks and benchmark datasets illustrate a state-of-the art trade-off between FLOPs reduction and accuracy, along with formidable computational savings.
Biography: Nick Freris is Professor in the School of Computer Science at USTC, and Vice Dean of the International College. He received the
Diploma in Electrical and Computer Engineering from the National Technical University of Athens (NTUA), Greece, in 2005, and the M.S. degree in Electrical and Computer Engineering, the M.S. degree in Mathematics, and the Ph.D. degree in Electrical and Computer Engineering all from the
University of Illinois at Urbana-Champaign
(UIUC) in 2007, 2008, and 2010, respectively.
His research lies in AIoT/CPS/IoT: machine learning, distributed optimization, data mining, wireless networks, control, and signal processing, with
applications in power systems, sensor networks, transportation, cyber security, and robotics. Dr. Freris has published several papers in high-profile
conferences and journals held by IEEE, ACM, and SIAM and holds three patents. His research has been sponsored by the Ministry of Science and Technology of China, Anhui Dept. of Science and Technology, Tencent, and NSF, and was recognized with the National High-level Talent award, the USTC Alumni Foundation Innovation Scholar award, and the IBM High Value Patent award. Previously, he was with the faculty of NYU and, before
that, he held senior researcher and postdoctoral researcher positions at EPFL and IBM Research, respectively.
Dr. Freris is a Senior Member of ACM and IEEE, and a member of CCF and SIAM.<Personal Webpage>
Prof. Minglun Gong
University of Guelph, Canada
Speech Title: TBA
Biography:Dr. Minglun Gong is a Professor and Director at the School of Computer Science, University of Guelph. Before he moved to Guelph in
2019, he was a Professor and Head at the Department of Computer Science, Memorial University of Newfoundland. He obtained his Ph.D. from the University of Alberta in 2003, his M.Sc. from the Tsinghua University in 1997, and his B.Engr. from the Harbin Engineering University in 1994.
Minglun’s research interests cover various topics in the broad area of visual computing (including computer graphics, computer vision, visualization, image processing, and pattern recognition). So far, he has published 150+ referred technical papers in journals and conference proceedings,
including 20+ articles in ACM/IEEE transactions. He is the inventor of an awarded patent and 6 pending patents. Currently an associate editor
for Pattern Recognition and IEEE Signal Processing Letters, he has also served as program committee member for top-tier conferences and
reviewer for prestigious journals. He was the recipient of the Izaak Walton Killam Memorial Award and best paper awards at the 2015 Canadian
Conference on Artificial Intelligence, the 2016 International Symposium on Visual Computing, and the 2019 Conference on Computer and Robot
Vision. <Personal Webpage>
Prof. Kaizhu Huang
Duke Kunshan University, China
Speech Title: TBA
Biography:Kaizhu Huang is currently a Professor at Duke Kunshan University, China. He acts as associate dean of research in School of Advanced Technology, XJTLU and is also the founding director of Suzhou Municipal Key Laboratory of Cognitive Computation and Applied Technology. Prof.
Huang obtained his PhD degree from Chinese University of Hong Kong (CUHK) in 2004. He worked in Fujitsu Research Centre, CUHK, University of Bristol, National Laboratory of Pattern Recognition, Chinese Academy of Sciences from 2004 to 2012. Prof. Huang has been working in machine
learning, neural information processing, and pattern recognition. He was the recipient of 2011 Asia Pacific Neural Network Society Young
Researcher Award. He received the best paper or book award six times. So far, he has published 9 books and over 200 international research papers (80+ international journals) e.g., in journals (JMLR, Neural Computation, IEEE T-PAMI, IEEE T-NNLS, IEEE T-BME, IEEE T-Cybernetics,
Cognitive Computation) and conferences (NeurIPS, IJCAI, SIGIR, UAI, CIKM, ICDM, ICML, ECML, CVPR). He serves as associated editors/
advisory board members in a number of journals and book series. He was invited as keynote speaker in more than 30 international conferences or workshops.
Prof. Philippe Fournier-Viger
Shenzhen University, China
Speech Title: Advances and challenges for the automatic discovery of interesting patterns in data
Abstract:Intelligent systems and tools can play an important role in various domains such as for factory automation, e-business, and
manufacturing. To build intelligent systems and tools, high-quality data is generally required. Moreover, these systems need to process complex
data and can yield large amounts of data such usage logs, images, videos, and data collected from industrial sensors. Managing the data to gain insights and improve these systems is thus a key challenge. It is also desirable to be able to extract information or models from data that are easily
understandable by humans. Based on these objectives, this talk will discuss the use of data mining algorithms for discovering interesting and useful patterns in data generated from intelligent systems and other applications.
The talk will first briefly review early study on designing algorithms for
identifying frequent patterns. Then, an overview of recent challenges and advances will be presented to identify other types of interesting patterns in more complex data. Topics that will be discussed include high utility patterns, locally interesting patterns, and periodic patterns. Lastly, the SPMF
open-source software will be mentioned and opportunities related to the combination of pattern mining algorithms with traditional artificial
intelligence techniques for intelligent systems will be discussed.
Biography:Philippe Fournier-Viger (Ph.D) is a Canadian researcher, distinguished professor at Shenzhen University (China). Five years after
completing his Ph.D., he came to China in 2015 and became full professor after receiving a talent title from the National Science Foundation of
China. He has published more than 375 research papers related to data mining algorithms for complex data (sequences, graphs), intelligent
systems and applications, which have received more than 11,000 citations. He is the founder of the popular SPMF data mining library, offering more than 250 algorithms to find patterns in data, cited in more than 1,000 research papers. He is former associate edito-in-chief of the Applied
Intelligence journal and has been keynote speaker for over 15 international conferences and co-edited four books for Springer. He is a co-founder of the UDML, PMDB and MLiSE series of workshops held at the ICDM, PKDD, DASFAA and KDD conferences. <Personal Webpage>