The presentations will be organized into two afternoon sessions: 1-5pm on Nov 25 (Friday) and 1-5pm on Nov 30 (Wednesday). The location will be announced.

Each person will make a 20-25 min presentation (including Q&A). To save time, you need to email your PPT to a session chair (to be named) before 10am that today (otherwise, your mark will be deducted).

• Hemanth Pidaparthy: RNNs/LSTMs for Image Captioning
  1. Deep-Visual Semantic Alginments for Generating Image Descriptions
  2. Visualizing and Understanding Recurrent Neural Networks
• Yifan Li: HMMs
  1. L. R. Rabiner and B. H. Juang, An Introduction to Hidden Markov Models
  2. L. R. Rabiner, A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition
• Matthew Tesfaldet: CNNs basics
  1. ???
  2. ???
• Leihan Chen: An overview of inference algorithm in undirected graphical model
  1. Tappen, M.F. and Freeman, W.T., 2003, October. Comparison of graph cuts with belief propagation for stereo, using identical MRF parameters. In Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on (pp. 900-906). IEEE.
  2. Kohli, P. and Torr, P.H., 2009. Robust higher order potentials for enforcing label consistency. International Journal of Computer Vision, 82(3), pp.302-324.
  3. Koltun, V., 2011. Efficient inference in fully connected crfs with gaussian edge potentials. Adv. Neural Inf. Process. Syst.
• Chao Wang: Budgeted SGD for multi-class SVMs
  1. Breaking the Curse of Kernelization: Budgeted Stochastic Gradient Descent for Large-Scale SVM Training
  2. Ensembles of Budgeted Kernel Support Vector Machines for Parallel Large Scale Learning
• Feng Wei: PageRank and Personalized PageRank
  1. Page, Lawrence, et al. “The PageRank citation ranking: bringing order to the web,” 1999.
  2. Alhelbawy, Ayman, and Robert J. Gaizauskas. “Graph Ranking for Collective Named Entity Disambiguation.” ACL. 2014.
  3. Pershina, Maria, Yifan He, and Ralph Grishman. “Personalized Page Rank for named entity disambiguation.” Proc. 2015 Annual Conference of the North American Chapter of the ACL, NAACL HLT. Vol. 14. 2015.
• Jack Wu: Language Understanding using RNNs and GRUs
  1. Rudolf Kadlec, Martin Schmid, Ondrej Bajgar & Jan Kleindienst, “Text Understanding with the Attention Sum Reader Network,” arXiv:1603.01547.
  2. Danqi Chen and Jason Bolton and Christopher D. Manning, “A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task,” arXiv:1606.02858.
• Hao Li: Reinforcement Learning: basics
  1. Bhuwan Dhingra, Lihong Li, Xiujun Li, Jianfeng Gao, Yun-Nung Chen, Faisal Ahmed, Li Deng, “End-to-End Reinforcement Learning of Dialogue Agents for Information Access,” arXiv:1609.00777.
  2. Jiwei Li, Will Monroe, Alan Ritter, Michel Galley, Jianfeng Gao, Dan Jurafsky, “Deep Reinforcement Learning for Dialogue Generation”, arXiv:1606.01541.
• Eunkyung Park: Latent Dirichlet Allocation and topic models
  1. David M. Blei, Andrew Y. Ng, Michael I. Jordan, “Latent Dirichlet Allocation”, Journal of Machine Learning Research 3 (2003) 993-1022.
  2. Matthew A. Taddy, “On Estimation and Selection for Topic Models,” Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS) 2012.
• Meng Jia: Adversarial Networks
  1. Generative Adversarial Nets
  2. Conditional Generative Adversarial Nets
  3. Generating Videos with Scene Dynamics
  4. Generating images with recurrent adversarial networks
• Mahdieh Abbaszadegan: RNNs basics (BPTT/RTRL/EKF, etc..)
  1. A tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the "echo state network" approach
  2. A guide to recurrent neural networks and backpropagation
  3. Simple recurrent network trained by RTRL and Extended Kalman Filter Algorithm
• Po Wu: Metric Learning
  1. ???
  2. ???
• Gong Cheng: Sparse Auto-encoder
  1. Ian Goodfellow, Yoshua Bengio, and Aaron Courville, A book chapter (Auto-Encoder) from Deep Learning, 14.1-14.3 pp 502-509.
  2. Andrew Ng, Sparse Auto-encoder