Yang's research group wiki

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=====Transfer learning reading group=====
A reading group to discuss recent or classical papers on transfer learning methodology and other interesting theory topics.
 
**Time:  Monday 4-5pm**
 
**// :!: 端午节 Schedule change (week 5/29-6/2): Wednesday 4:30-5:30pm//** 

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====Schedule====


===5/15/2017===
  * Presenter: Yajie
  * Paper 1 (main): Feng Liu, Guanquan Zhang, Haiyan Lu, Jie Lu,  {{ :alternating_diffusion_map.pdf | Heterogeneous Unsupervised Cross-domain Transfer Learning.}}
  * Paper 2: Ye, Ke, and Lekheng Lim. {{ ::grassmannan_distance_shubert_variaty.pdf | Schubert varieties and distances between subspaces of different dimensions.}} SIAM Journal on Matrix Analysis and Applications 37.3 (2014): 1176-1197.

===5/22/2017===
  * Presenter: Yang
  * Paper 1 (main):  R.R. Lederman, R Talmon, {{ :alternating_diffusion_map.pdf | Learning the geometry of common latent variables using alternating-diffusion }}, Appl. Comput. Harmon. Anal. (2015) 
  * Paper 2: O. Katz, R. Talmon, Y.-L. Lo, H.-T. Wu, {{ :data-fusion-by-multi-sensor-diffusion-map.pdf 
| Diffusion-based nonlinear filtering for multimodal data fusion with application to sleep stage assessment}}


===5/31/2017===
  * Presenter: Yajie
  * Paper 1 (main):  Makur A, Kozynski F, Huang S L, et al. {{ :pacefinal.pdf | An efficient algorithm for information decomposition and extraction }} 
  * Paper 2: Huang S L, Zheng L. {{ :linear_information_coupling_problems.pdf | Linear information coupling problems}}

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====Topic suggestions====
Suggest a topic or a paper that you want us to discuss in the future.

===Functional maps===
  * Paper: Maks Ovsjanikov, Mirela Ben-Chen, Justin Solomon, Adrian Butscher and Leonidas Guibas. {{ :functional_map.pdf | Functional Maps: A Flexible Representation of Maps Between Shapes}}. ACM Transactions on Graphics. 31(4), 2012 
  * Reason: this paper presents a methodology to find consistent functional representations between the feature spaces of two 3D shapes. We want to understand the theoretical background of this method, and explore the possibility of applying it to generic feature spaces other than images or shapes. --- //Yang 2017/6/5 //

===Cross-domain feature learning===
  * Paper 1 (main): Nguyen, Hien V., et al, {{ :dash-n_joint_hierarchical_domain_adaptation_and_feature_learning.pdf | DASH-N: Joint hierarchical domain adaptation and feature learning }}, IEEE Transactions on Image Processing 24.12 (2015): 5479-5491.
  * Paper 2: Lin, Liang, et al， {{ :cross-domain_visual_matching_via_generalized_similarity_measure_and_feature_learning.pdf | Cross-domain visual matching via generalized similarity measure and feature learning }}, IEEE transactions on pattern analysis and machine intelligence (2016).
 

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====Useful resources====
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