<commit_before># Literature research list

Reading list about video/data summarization topic.

## Articles

### Diversity Modelling

1. [Learning Probabilistic Submodular Diversity Models Via Noise Contrastive Estimation](https://las.inf.ethz.ch/files/tschiatschek16diversity.pdf). <NAME> et al. AISTATS 2016. JMLR: W&CP volume 51.
2. [k -DPPs : Fixed-Size Determinantal Point Processes](http://homes.cs.washington.edu/~taskar/pubs/kdpps_icml11.pdf) <NAME> and <NAME>. ICML 2011
3. [Expectation-Maximization for Learning Determinantal Point Processes](https://papers.nips.cc/paper/5564-expectation-maximization-for-learning-determinantal-point-processes.pdf). <NAME>, et al. NIPS 2014.
4. [Learning determinantal point processes](http://www.alexkulesza.com/pubs/dpplearn_uai11.pdf). <NAME> and <NAME>. Conference on Uncertainty in Artificial Intelligence (UAI 2011).
5. [Determinantal Point Processes for Machine Learning](http://dl.acm.org/citation.cfm?id=2481023). <NAME>. Found. Trends® Mach. Learn., vol. 5, no. 2–3, pp. 123–286, 2012.

### Video Summarization
1. [Diverse Sequential Subset Selection for Supervised Video Summarization](https://papers.nips.cc/paper/5413-diverse-sequential-subset-selection-for-supervised-video-summarization.pdf). <NAME>, et al. NIPS 2014.
2. [VSUMM: A mechanism designed to produce static video summaries and a novel evaluation method](http://www.sciencedirect.com/science/article/pii/S0167865510002783). de Avila, et al. Pattern Recognition Letter. 2011.
3. [TVSum: Summarizing web videos using titles](http://ieeexplore.ieee.org/document/7299154). Y. Song, et al. CVPR 2015.
4. [To Click or Not To Click: Automatic Selection of Beautiful Thumbnails from Videos](https://arxiv.org/pdf/1609.01388v1.pdf). Y. Song, et al. CIKM 2016.
5. [Video Summarization: Techniques and Classification](http://link.springer.com/chapter/10.1007/978-3-642-33564-8_1). <NAME>, et al. Computer Vision and Graphics, Springer, 2012, pp. 1–13.
6. [Video Summarization by Learning Submodular Mixtures of Objectives](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Gygli_Video_Summarization_by_2015_CVPR_paper.pdf)
7. [Video Summarization using Deep Semantic Features](https://arxiv.org/pdf/1609.08758.pdf). Otani, et al. ACCV 2016.


## Posts (Blogs, Online forums)

1. [Quora: Comparision between gradient methods](https://www.quora.com/What-are-differences-between-update-rules-like-AdaDelta-RMSProp-AdaGrad-and-AdaM). Accessed: 2016/12/07.

## Online Courses
<commit_msg>add submodular review paper and submodular video summarization<commit_after># Literature research list

Reading list about video/data summarization topic.

## Articles

### Diversity Modelling and Submodularity

1. [Learning Probabilistic Submodular Diversity Models Via Noise Contrastive Estimation](https://las.inf.ethz.ch/files/tschiatschek16diversity.pdf). <NAME> et al. AISTATS 2016. JMLR: W&CP volume 51.
2. [k -DPPs : Fixed-Size Determinantal Point Processes](http://homes.cs.washington.edu/~taskar/pubs/kdpps_icml11.pdf) <NAME> and <NAME>. ICML 2011
3. [Expectation-Maximization for Learning Determinantal Point Processes](https://papers.nips.cc/paper/5564-expectation-maximization-for-learning-determinantal-point-processes.pdf). <NAME>, et al. NIPS 2014.
4. [Learning determinantal point processes](http://www.alexkulesza.com/pubs/dpplearn_uai11.pdf). <NAME> and <NAME>. Conference on Uncertainty in Artificial Intelligence (UAI 2011).
5. [Determinantal Point Processes for Machine Learning](http://dl.acm.org/citation.cfm?id=2481023). <NAME>. Found. Trends® Mach. Learn., vol. 5, no. 2–3, pp. 123–286, 2012.
6. [Submodular Function Maximization](https://las.inf.ethz.ch/files/krause12survey.pdf). <NAME>, et al. 2012.

### Video Summarization
1. [Diverse Sequential Subset Selection for Supervised Video Summarization](https://papers.nips.cc/paper/5413-diverse-sequential-subset-selection-for-supervised-video-summarization.pdf). <NAME>, et al. NIPS 2014.
2. [VSUMM: A mechanism designed to produce static video summaries and a novel evaluation method](http://www.sciencedirect.com/science/article/pii/S0167865510002783). de Avila, et al. Pattern Recognition Letter. 2011.
3. [TVSum: Summarizing web videos using titles](http://ieeexplore.ieee.org/document/7299154). Y. Song, et al. CVPR 2015.
4. [To Click or Not To Click: Automatic Selection of Beautiful Thumbnails from Videos](https://arxiv.org/pdf/1609.01388v1.pdf). Y. Song, et al. CIKM 2016.
5. [Video Summarization: Techniques and Classification](http://link.springer.com/chapter/10.1007/978-3-642-33564-8_1). <NAME>, et al. Computer Vision and Graphics, Springer, 2012, pp. 1–13.
6. [Video Summarization by Learning Submodular Mixtures of Objectives](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Gygli_Video_Summarization_by_2015_CVPR_paper.pdf). <NAME>, et al. CVPR 15.
7. [Video Summarization using Deep Semantic Features](https://arxiv.org/pdf/1609.08758.pdf). Otani, et al. ACCV 2016.
8. [Gaze-enabled Egocentric Video Summarization via Constrained Submodular Maximization](http://pages.cs.wisc.edu/~jiaxu/projects/ego-video-sum/ego-video-sum-cvpr2015.pdf). Jia Xu, et al. CVPR 15.


## Posts (Blogs, Online forums)

1. [Quora: Comparision between gradient methods](https://www.quora.com/What-are-differences-between-update-rules-like-AdaDelta-RMSProp-AdaGrad-and-AdaM). Accessed: 2016/12/07.

## Online Courses

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