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Seminars and Events (Including Past and Upcoming)
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| January 15, 2020 |
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Title: HKU-Oxford Memorandum of Understanding Signing Ceremony cum HKU-Oxford Joint Lab Inauguration Ceremony
Time: 02:00pm
Venue: Room 328, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): -----
Remark(s): Rundown:
| 2:00-2:05pm |
Speech by Professor Bob Coecke, Head of Quantum Group, Department of Computer Science, University of Oxford |
| 2:05-2:10pm |
Speech by Professor Christopher Chao, Dean of Engineering, Faculty of Engineering, The University of Hong Kong |
| 2:10-2:15pm |
Speech by Prof. Alfonso Ngan, Acting-Designate Pro-Vice-Chancellor (Research), The University of Hong Kong |
| 2:15-2:20pm |
Photo taking |
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| January 14, 2020 |
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Title: QICI Distinguished Lecture: What is time?
Time: 04:45pm
Venue: Lecture Theatre A, Chow Yei Ching Building, University of Hong Kong Hong Kong
Speaker(s): Professor Carlo Rovelli, Professor of Exceptional Class, University of Aix-Marseille
Remark(s):
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| January 09, 2020 |
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Title: Blockchain Training Series
Time: 06:30pm
Venue: CPD-3.04, Centennial Campus, The University of Hong Kong
Speaker(s): Various
Remark(s):
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| December 19, 2019 |
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| December 16, 2019 |
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| November 30, 2019 |
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| November 22, 2019 |
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Title: Quantum Information Seminar
Time: 02:00pm
Venue: Rm 313, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): Dr. Jaehak Lee, Korea Institute of Advanced Study (KIAS)
Remark(s):
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| November 11, 2019 |
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Title: Computer Vision of Refractive Media
Time: 11:00am
Venue: Room 328, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): Professor Herb Yang, Department of Computing Science, University of Alberta
Remark(s): Abstract:
Computer vision of opaque objects has been extensively studied. However, there is signicantly less attention on refractive media. In this talk, I will give an overview of my recent research in the area of computer vision of refractive media, which includes solids and uids. My interest in this topic began several years ago when my group was asked to develop an undersea 3D vision system for Neptune Canada, which had been merged with Venus Canada to form Ocean Networks Canada. During our research, we discovered that simply applying land-based computer vision techniques to undersea appears trival but is, unfortunately, incorrect. Surprisingly, most photogrammetry methods at the time incorrectly adapted land-based methods to undersea applications with minor tweaking of parameters. By accommodating refraction in our methods, we have developed several physics based algorithms that outperform the accuracy of existing algorithms. Rather than a hindrance, we also discovered that refraction can be leveraged in underwater imaging. For example, we take advantage of dispersion to calibrate an underwater camera with improved accuracy. As well, dispersion can also be used to reconstruct an object in 3D with only one single view, i.e. one single camera, which is not possible for a typical land-based camera. More recently, motivated by our underwater results, we further explore developing new physics based methods to reconstruct shapes of transparent objects, which include transparent solids and dynamic water surfaces and underwater scenes.
About the Speaker:
Herb Yang (SM IEEE) received his B.Sc. (first honours) from the University of Hong Kong, his M.Sc. from Simon Fraser University, and his M.S.E.E. and Ph.D. from the University of Pittsburgh. He was a faculty member in the Department of Computer Science at the University of Saskatchewan from 1983 to 2001 and served as Graduate Chair from 1999 to 2001. Since July, 2001, he has been a Professor in the Department of Computing Science at the University of Alberta. He served as Associate Chair (Graduate Studies) in the same department from 2003 to 2005 and as Science Internship Director from 2016 to 2019. His research interests cover a wide range of topics from computer graphics to computer vision, which include physically based animation of Newtonian and non-Newtonian fluids, texture analysis and synthesis, human body motion analysis and synthesis, computational photography, stereo and multiple view computer vision, underwater imaging and medical imaging. He has published over 150 papers in international journals and conference proceedings in the areas of computer vision, computer graphics and medical imaging. He is a Senior Member of the IEEE and serves on the Editorial Board of the journal Pattern Recognition and IET-Computer Vision. In addition, he has served as reviewer and program committee member to many international conferences and reviewer to many international journals, and funding agencies. In 2007, he was invited to serve on the expert review panel to evaluate computer science research in Finland.
All are welcome!
For enquiries, please call 2859 2180 or emailenquiry@cs.hku.hk
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| November 08, 2019 |
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Title: A Mutual Information Maximization Perspective of Language Representation Learning
Time: 03:00pm
Venue: Room 308, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): Dr Lingpeng Kong, Senior Research Scientist, Google DeepMind
Remark(s): Abstract:
In this talk, we show state-of-the-art word representation learning methods maximize an objective function that is a lower bound on the mutual information between different parts of a word sequence (i.e., a sentence). Our formulation provides an alternative perspective that unifies classical word embedding models (e.g., Skip-gram) and modern contextual embeddings (e.g., BERT, XLNet). In addition to enhancing our theoretical understanding of these methods, our derivation leads to a principled framework that can be used to construct new self-supervised tasks. We provide an example by drawing inspirations from related methods based on mutual information maximization that have been successful in computer vision, and introduce a simple self-supervised objective that maximizes the mutual information between a global sentence representation and n-grams in the sentence. Our analysis offers a holistic view of representation learning methods to transfer knowledge and translate progress across multiple domains (e.g., natural language processing, computer vision, audio processing).
About the Speaker:
Lingpeng Kong is Senior Research Scientist at Google DeepMind. His research focuses on the computational modeling of structures in natural language processing (NLP) with applications related to sequence labeling, syntactic parsing, and machine translation. He received his Ph.D. from Carnegie Mellon University where he was co-advised by Professor Noah Smith and Professor Chris Dyer.
All are Welcome!
Tel: 2859 2180 for enquiries
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