====== Advanced Topics for self-study and Presentation ======


==== Advanced Topics for self-study and Presentation ====

The presentations will be organized into two afternoon sessions: **1-5pm on Nov 25 (Friday) <fc red> @HNE 037 </fc> and 1-5pm on Nov 30 (Wednesday) <fc red> @LAS3033  </fc> **. 

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 day (otherwise, your mark will be deducted). 


**<fc red> Nov 25: </fc>**  all presentations NOT related to deep learning. Feng Wei <fwei@cse.yorku.ca> will coordinate the session. Email him your slides by 10am Nov 25. The presentation will take place from 1pm @HNE 037 in the following order:

  - Chao Wang
  - Yifan Li
  - Eunkyung Park
  - Leihan Chen
  - Feng Wei
  - Po Wu
  - Yangguang Li

**<fc red> Nov 30: </fc>**  all presentations related to deep learning. Chao Wang <chwang@cse.yorku.ca> will coordinate the session. Email him your slides by 10am Nov 30. The presentation will take place from 1pm @LAS3033 in the following order:

  - Matthew Tesfaldet
  - Mahdieh Abbaszadegan
  - Hemanth Pidaparthy
  - Jack Wu
  - Gong Cheng
  - Hao Li
  - Meng Jia

=== The advanced topics include: ===

  * **Hemanth Pidaparthy**: RNNs/LSTMs for Image Captioning 
    - [[http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Vinyals_Show_and_Tell_2015_CVPR_paper.pdf|Show and Tell: A Neural Image Caption Generator]]
    - [[http://vision.stanford.edu/pdf/KarpathyICLR2016.pdf|Visualizing and Understanding Recurrent Neural Networks]]
  * **Yifan Li**: HMMs
    - L. R. Rabiner and B. H. Juang, [[http://ai.stanford.edu/~pabbeel/depth_qual/Rabiner_Juang_hmms.pdf|An Introduction to Hidden Markov Models]]
    - L. R. Rabiner, [[http://www.cs.cornell.edu/courses/cs481/2004fa/rabiner.pdf|A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition]]
  * **Matthew Tesfaldet**: CNNs basics
    - Hubel, D. H.; Wiesel, T. N. (1968-03-01). "[[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1557912/pdf/jphysiol01104-0228.pdf|Receptive fields and functional architecture of monkey striate cortex]]”
    - LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey (2015). "[[http://www.nature.com/nature/journal/v521/n7553/pdf/nature14539.pdf|Deep learning]]”
    - Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E. "[[http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf|ImageNet Classification with Deep Convolutional Neural Networks”]]
    - LeCun, Yann; Léon Bottou; Yoshua Bengio; Patrick Haffner (1998). "[[http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf|Gradient-based learning applied to document recognition]]”
  * **Leihan Chen**: An overview of inference algorithm in undirected graphical model
    - Tappen, M.F. and Freeman, W.T., 2003, October. [[http://www.eecs.ucf.edu/~mtappen/iccv03.pdf|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.
    - Kohli, P. and Torr, P.H., 2009. [[http://research.microsoft.com/en-us/um/people/pkohli/papers/klt_CVPR08.pdf|Robust higher order potentials for enforcing label consistency]]. International Journal of Computer Vision, 82(3), pp.302-324.
    - Koltun, V., 2011. [[https://www.robots.ox.ac.uk/~vgg/rg/papers/kraehenbuehl__nips2012__densecrf.pdf|Efficient inference in fully connected crfs with gaussian edge potentials]]. Adv. Neural Inf. Process. Syst. 
  * **Chao Wang**: Budgeted SGD for multi-class SVMs
    - [[http://www.jmlr.org/papers/volume13/wang12b/wang12b.pdf|Breaking the Curse of Kernelization: Budgeted Stochastic Gradient
Descent for Large-Scale SVM Training]]
    -    [[https://www.etsmtl.ca/ETS/media/ImagesETS/Labo/LIVIA/Publications/2013/Levesque_NIPS_2013.pdf | Ensembles of Budgeted Kernel Support Vector Machines for Parallel Large Scale Learning]]
  * **Feng Wei**: PageRank and Personalized PageRank
    - Page, Lawrence, et al. "[[http://ilpubs.stanford.edu:8090/422/1/1999-66.pdf|The PageRank citation ranking: bringing order to the web]]," 1999.
    - Alhelbawy, Ayman, and Robert J. Gaizauskas. "[[http://www.aclweb.org/anthology/P14-2013|Graph Ranking for Collective Named Entity Disambiguation]]." ACL. 2014.
    - Pershina, Maria, Yifan He, and Ralph Grishman. "[[http://www.aclweb.org/anthology/N15-1026|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
    - Rudolf Kadlec, Martin Schmid, Ondrej Bajgar & Jan Kleindienst, "[[https://arxiv.org/abs/1603.01547|Text Understanding with the Attention Sum Reader Network]]," arXiv:1603.01547.
    - Danqi Chen and Jason Bolton and Christopher D. Manning, "[[https://arxiv.org/abs/1606.02858|A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task]]," arXiv:1606.02858.
  * **Hao Li**: Reinforcement Learning: basics
    - 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," [[https://arxiv.org/abs/1609.00777|arXiv:1609.00777]].
    - Jiwei Li, Will Monroe, Alan Ritter, Michel Galley, Jianfeng Gao, Dan Jurafsky, "Deep Reinforcement Learning for Dialogue Generation", [[https://arxiv.org/abs/1606.01541|arXiv:1606.01541]].
  * **Eunkyung Park**: Latent Dirichlet Allocation and topic models
    - David M. Blei, Andrew Y. Ng, Michael I. Jordan, "[[https://www.cs.princeton.edu/~blei/papers/BleiNgJordan2003.pdf|Latent Dirichlet Allocation]]", Journal of Machine Learning Research 3 (2003) 993-1022.
    - Matthew A. Taddy, "[[http://jmlr.org/proceedings/papers/v22/taddy12/taddy12.pdf|On Estimation and Selection for Topic Models]]," Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS) 2012.
  * **Meng Jia**: Adversarial Networks
    - [[https://arxiv.org/pdf/1406.2661v1.pdf
|Generative Adversarial Nets]]
    - [[https://arxiv.org/pdf/1411.1784v1.pdf
|Conditional Generative Adversarial Nets]]
    - [[http://web.mit.edu/vondrick/tinyvideo/paper.pdf
|Generating Videos with Scene Dynamics]]
    - [[https://arxiv.org/pdf/1602.05110.pdf|Generating images with recurrent adversarial networks]]
  * **Mahdieh Abbaszadegan**: RNNs basics (BPTT/RTRL/EKF, etc..)
    - [[http://minds.jacobs-university.de/sites/default/files/uploads/papers/ESNTutorialRev.pdf|A tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the "echo state network" approach]] 
    - {{:rn_dallas.pdf|A guide to recurrent neural networks and backpropagation}}
    - {{:cernanskybenuskovannw03.pdf|Simple recurrent network trained by RTRL and Extended Kalman Filter Algorithm}}
  * **Po Wu**: Metric Learning
    - [[http://www.cs.cmu.edu/~liuy/frame_survey_v2.pdf|Distance Metric Learning: A Comprehensive Survey]]
    - [[https://lrs.icg.tugraz.at/research/kissme/paper/lrs_icg_koestinger_cvpr_2012.pdf|Large Scale Metric Learning from Equivalence Constraints]]
    - [[http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Liao_Person_Re-Identification_by_2015_CVPR_paper.pdf|Person Re-identification by Local Maximal Occurrence Representation and Metric Learning]]
  * **Gong Cheng**: Sparse Auto-encoder
    - Ian Goodfellow, Yoshua Bengio, and Aaron Courville, A book chapter ([[http://www.deeplearningbook.org/contents/autoencoders.html|Auto-Encoder]]) from Deep Learning, 14.1-14.3 pp 502-509. 
    - Andrew Ng, [[https://web.stanford.edu/class/cs294a/sparseAutoencoder.pdf|Sparse Auto-encoder]]
  * **Yangguang Li**:  ensemble learning 
    - [[http://www-vis.lbl.gov/~romano/mlgroup/papers/hbtnn-ensemble-learning.pdf|Ensemble learning]]
    - [[http://link.springer.com/chapter/10.1007/3-540-45014-9_1|Ensemble Methods in Machine Learning]]
    - [[http://ieeexplore.ieee.org/document/6392473/|Using coding-based ensemble learning to improve software defect prediction]]