Big data machine learning (BDML) studies machine learning techniques for big data applications. BDML has become one of the driving forces for the advancement of artificial intelligence. Stochastic learning, such as SGD and its extensions, has become one of the key techniques in BDML. This talk will introduce our recent works on parallel and distributed stochastic learning. Furthermore, a BDML platform called LIBBLE (https://github.com/LIBBLE/LIBBLE-Spark/), which is developed by our group and has been open sourced, will also be introduced in the talk.
Wu-Jun Li is currently an Associate Professor at the Department of Computer Science and Technology, Nanjing University, P. R. China. His research interests include machine learning, big data, and artificial intelligence. In these areas he has published more than 30 peer-reviewed papers, most in prestigious journals such as TKDE and top conferences such as AAAI, CVPR, ICML, IJCAI, NIPS, and SIGIR. He has served as PC member of most top conferences in machine learning and artificial intelligence, including AAAI, CVPR, ICCV, ICML, IJCAI, NIPS, KDD, etc. For more information, please refer to: http://cs.nju.edu.cn/lwj/ .
Weakly Supervised Image Understanding
Sematic segmentation of nature images is a fundamental problem in computer vision. While significant research progresses have been made in the last few years, the success of most existing method highly rely on large scale accurate pixel accurate annotations. However, humans effortlessly learn robust and accurate visual cognitive modes without the requirement of huge amount of pixel accurate semantic annotation. During childhood, we learn to robustly recognize and precisely locate the object regions with limited supervision from parents and other sources. Inspired by this process, our research focus on human cognitive inspired weakly supervised image understanding, by utilizing visual attention, category independent edge detection, region clustering etc., we observed consistent performance boost in weak supervised image upstanding.
Ming-Ming Cheng is a professor with CCCE, Nankai University. He received his PhD degree from Tsinghua University in 2012. Then he worked as a research fellow for 2 years, working with Prof. Philip Torr in Oxford. Dr. Cheng’s research primarily centers on algorithmic issues in image understanding and processing, including image segmentation, editing, retrieval, etc. He has published over 30 papers in leading journals and conferences, such as IEEE TPAMI, ACM TOG, ACM SIGGRAPH, IEEE CVPR, and IEEE ICCV. He has designed a series of popular methods and novel systems, indicated by 5000+ paper citations (1700+ citations to his first author paper on salient object detection). His work has been reported by several famous international media, such as BBC, UK telegraph, Der Spiegel, and Huffington Post.
Jonathan Huang is a senior research scientist at Google and currently works on deep learning for machine perception. He received his M.Sc degree and Ph.D from the School of Computer Science at Carnegie Mellon University in 2008 and 2011 respectively. From 2011 to 2014 he was an NSF Computing Innovation (CI) postdoctoral fellow at the geometric computing group at Stanford University where he also received his B.S. degree in Mathematics in 2005. His research interests lie primarily in deep learning, and probabilistic reasoning with combinatorially structured data with applications in computer vision and online education. To see a list of publications and projects, visitwww.jonathan-huang.org.
Data-based Source Localization for a Moving Source: Theory and Experimental Results
T.C. Yang received the Ph.D. degree in high energy physics from the University of Rochester, Rochester, NY, USA, in 1971.
He is currently a Professor and previously a Pao Yu-Kong Chair Professor at Zhejiang University, Hangzhou, China. From 2012 to 2014, he was a National Science Counsel Chair Professor at the National Sun Yat-Sen University, Kaohsiung, Taiwan. Before that, he spent 32 years working at the Naval Research Laboratory, Washington, DC, USA, serving as the Head of the Arctic Section, Dispersive Wave Guide Effects Group, and the Head of the Acoustic Signal Processing Branch, and consultant to the division on research proposals. His current research focuses on environmental impacts on underwater acoustic communications and networking, exploiting the channel physics to characterize and improve performance, environmental acoustic sensing and signal processing using distributed networked sensors, and methods for improved channel tracking and data-based source localization. In earlier years, he pioneered matched mode processing for a vertical line array, and matched-beam processing for a horizontal line array. His other areas of research included geoacoustic inversions, waveguide invariants, effects of internal waves on sound propagation in shallow water, Arctic acoustics, etc.
Prof. is a Fellow of the Acoustical Society of America.
Z. Jane Wang
University of British Columbia
Joint Blind Source Separation (JBSS) for Multiset, Multimodal Data Analysis
Blind Source Separation (BSS) has been attracting increasing attention due to its promising applications in numerous areas. Joint blind source separation (JBSS) represents both challenges and opportunities for multiset, multimodal data analysis, e.g., the neurophysiological signal processing community attempts to enhance understanding of normal brain function and the pathophysiology of many brain diseases by extracting information from complementary modalities using JBSS. We will discuss (1.) the over-determined JBSS case by investigating different statistical assumptions and tradeoffs between different JBSS methods, with focus on applications on cortico-muscular coupling analysis and biosensor based heart beat rate monitoring; and (2.) the less-studied under-dertermined JBSS (UJBSS) case, where the number of sensors M is smaller than the number of sources N, with focus on developing new UJBSS methods for 2 and multiple datasets by exploring the second-order statistics of the underlying sources. We present a novel UJBSS approach for artifacts removal (e.g., removing Electromyogram (EMG) from Electroencephalography (EEG) signals).
Z. Jane Wang received the B.Sc. degree from Tsinghua University, China, in 1996, and the M.Sc. and Ph.D. degrees from the University of Connecticut in 2000 and 2002, respectively, all in electrical engineering. She has been Research Associate of Electrical & Computer Engineering Department at the University of Maryland, College Park. Since Aug. 1, 2004, she has been with the Department Electrical and Computer Engineering at the University of British Columbia, Canada, and is currently a Professor. She is an IEEE Fellow. Her research interests are in the broad areas of statistical signal processing theory and applications. She co-received the EURASIP Journal on Applied Signal Processing (JASP) Best Paper Award 2004, and the IEEE Signal Processing Society Best Paper Award 2005. She has published over 100 journal papers and about 90 conference papers. She served as or is serving as Associate Editor for IEEE journals including IEEE Trans. on Signal Processing, IEEE Trans. on Information Forensics & Security, IEEE Trans. on Biomedical Engineering, IEEE Signal Processing Letters and IEEE Trans. on Multimedia.
University of Southern California
An Overview on GSP
Antonio Ortega received the Telecommunications Engineering degree from the Universidad Politecnica de Madrid, Madrid, Spain in 1989 and the Ph.D. in Electrical Engineering from Columbia University, New York, NY in 1994. His Ph.D. work was supported by the Fulbright Commission and the Ministry of Education of Spain. He joined the University of Southern California as an Assistant Professor in 1994 and is currently a Professor. At USC he is a member of the Integrated Media Systems Center, an NSF Engineering Research Center. He was Director of the Signal and Image Processing Institute (2004-2006) and Associate Chair of Electrical Engineering-Systems (2004-2007). In 1995 he received the NSF Faculty Early Career Development (CAREER) Award. He is a Fellow of the IEEE, a member of the ACM. He has been an Associate Editor of the IEEE Transactions on Image Processing and of the IEEE Signal Processing Letters. He is also a member of the IEEE Signal Processing Society Multimedia Signal Processing (MMSP) and Image and Multidimensional Signal Processing (IMDSP) technical committees. He was Chair of the IMDSP committee in 2004-5. He received the 1997 Northrop Grumman Junior Research Award awarded by the School of Engineering at USC. In 1998 he received the Leonard G. Abraham IEEE Communications Society Prize Paper Award for the best paper published in the IEEE Journal on Selected Areas in Communications in 1997, for his paper co-authored with Chi-Yuan Hsu and Amy R. Reibman. He also received the IEEE Signal Processing Society, Signal Processing Magazine Award in 1999 for a paper co-authored with Kannan Ramchandran, which appeared in the Signal Processing Magazine in November 1998. He also received the 2006 EURASIP Journal on Advances in Signal Processing Best Paper award for his paper A Framework for Adaptive Scalable Video Coding Using Wyner-Ziv Techniques co-authored with Huisheng Wang and Ngai-Man Cheung. He is the technical program co-chair for ICIP 2008. His research interests are in the area of digital image and video compression, with a focus on systems issues related to transmission over networks, application-specific compression techniques, and fault/error tolerant signal processing algorithms.