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--- presentations [2016/11/08 17:39] – hj
+++ presentations [2016/11/27 21:39] (current) – hj
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 ==== Advanced Topics for self-study and Presentation ====
-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.
+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 today (otherwise, your mark will be deducted).
+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://cs.stanford.edu/people/karpathy/cvpr2015.pdf|Deep-Visual Semantic Alginments for Generating Image Descriptions]]
+    - [[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 B. H. Juang, [[http://ai.stanford.edu/~pabbeel/depth_qual/Rabiner_Juang_hmms.pdf|An Introduction to Hidden Markov Models]]
+    - 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.
@@ Line 51: / Line 76: @@
     - {{: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]]