||WH 221 | Friday 4:00-6:50 PM|
||Junzhou Huang | ERB 650 | Office hours: Friday 1:00-4:00 PM|
||Basic math and programming background; Basic learning and vision background preferred|
This course will provide an overview of the current state-of-the-art of big data searching techniques in computer vision, machine learning and data mingearning by studying a set of cutting-edge advanced topics in these areas. Several selected research topics reflect the current state in these fields. The main objective of this course is to review cutting-edge searching& learning research in big data through lectures covering the underlying statistical & mathematical concepts and representative algorithms, paper reading, and implementation. The instructor will work with students on building ideas, performing experiments, and writing papers. Students can decide to submit his/her results to a learning/mining/vision related conference, or just play with funs.
The course is application-driven and includes advacnced topics
in imaging, learning and vision, such as different imaging techniques and advanced
learning tools in different applications. It will also include selected topics
relating to the emerging compressed sensing and sparse learning theory and techniques.
The course will provide the participants with a thorough background in current
research in these areas, as well as to promote greater awareness and interaction
between multiple research groups within the university. The course material
is well suited for students in computer science, computer engineering, electrical
engineering and biomedical engineering.
M. Turk and A. Pentland, "Face recognition using eigenfaces", CVPR 1991.
M. Brand, "Incremental singular value decomposition of uncertain data with missing values", ECCV 2002.
D. Ross, J. Lim, R. Lin, M. Yang, "Incremental Learning for Robust Visual Tracking", International Journal of Computer Vision, 2007.
A. Beck and M. Teboulle, "A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems", SIAM Journal on Imaging Sciences, No. 1, pp. 183-202, 2009.
A. Beck and M. Teboulle, "Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems", IEEE Trans. Image Processing, Vol. 18, No. 11, pp. 2419-2434, 2009
Yurii Nesterov, "Gradient
Methods for Minimizing Composite Objective Function", 2007.
Fri Feb 10: Scalable Searching Via Hiearchical Kmean Tree (Slides)
D. Lowe, "Object recognition from local scale-invariant features", ICCV 1999.
D. Nist́er and H. Steẃenius, "Scalable
Recognition with a Vocabulary Tree", CVPR 2006.
Yann LeCun, Yoshua Bengio & Geoffrey Hinton, "Deep Learning", Nature 2015 (Akib Zaman)
Krizhevsky, A., Sutskever, I. and Hinton, G. E., "ImageNet Classification with Deep Convolutional Neural Networks", NIPS 2012 (Srinivas Varadharajan)
Fri Mar 3:
Silver et. al., "Mastering the game of Go with Deep Neural Networks & Tree Search", Nature 2016 (Kunwar Dev Singh)
Graves el. al., "Hybrid Computing Using a Neural Network with Dynamic External Memory", Nature 2016 (Nitin Kanwar, Jayvardhan Chinchwade)
Jon Long*, Evan Shelhamer*, Trevor Darrell, "Fully Convolutional Networks for Semantic Segmentation", CVPR 2015. (Md Abdus Aslam (Lincoln))
Ma, Chenxin, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtárik, and Martin Takáč. "Adding vs. averaging in distributed primal-dual optimization." ICML 2015. (Likhit Shankar Gowda, Likhith Mayanna Gowda)
Jasper Snoek, Hugo Larochelle and Ryan P. Adams. "Practical Bayesian Optimization of Machine Learning Algorithms", NIPS 2012. (Guodong Liu)
Mikael Henaff, Joan Bruna, Yann LeCun, "Deep Convolutional Networks on Graph-Structured Data", 2015 (Shivam Bijoria)
Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus, Yann LeCun, "OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks", ICLR 2014 (Akash Rana, Anuradha Nokku)
Ross Girshick, "Fast R-CNN", arXiv:1504.08083 (Vikas Sable, Mailtili Deshpand)
Fri Apr 7:
Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks", NIPS 2015 (Fan Ni)
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, "SSD: Single Shot MultiBox Detector", ECCV 2016 (Girish Tejas Prakash)
Fri Apr 14:
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi, "You Only Look Once: Unified, Real-Time Object Detection", CVPR 2016 (Sadhana Singh, Saurabh Kumar Singh)
Joseph Redmon, Ali Farhadi, "YOLO9000: Better, Faster, Stronger", 2016 (Pranil Maharjan, Vipulkumar Mahadik)
Fri Apr 21:
Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation", CVPR, 2014. (Mohammad Minhazul Haq)
Zhe Zhu, Dun Liang, Songhai Zhang, Xiaolei Huang, Baoli Li, Shimin Hu, "Traffic-Sign Detection and Classification in the Wild", CVPR 2016 (SanathKumar and Bhushan yavgal)
Zhe Zhu, Dun Liang, Songhai Zhang, Xiaolei Huang, Baoli Li, Shimin Hu, "Traffic-Sign Detection and Classification in the Wild", CVPR 2016 (Muhammad Tayyab)
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio, “Generative Adversarial Nets”, arXiv:1406.2661 (Zhifei Deng)
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets (Arun Balchandran)
Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov, "Learning Convolutional Neural Networks for Graphs", ICML 2016 (Shashank Madhav and Ashay Rajimwale)
Vijay Badrinarayanan, Ankur Handa and Roberto Cipolla, "SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling." arXiv preprint arXiv:1505.07293, 2015 (Rodrigo Linhares)
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