Update README.md

README.md

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 A curated list of the most cited deep learning papers (2012-2016)
 
-We believe that there exist *classic* deep learning papers which are worth reading regardless of their application domain. Rather than providing overwhelming amount of papers, We would like to provide a *curated list* of the awesome deep learning papers which are considered as *must-reads* in certain research domains.
+We believe that there exist *classic* deep learning papers which are worth reading regardless of their application domain. Rather than providing an overwhelming amount of papers, We would like to provide a *curated list* of the awesome deep learning papers which are considered as *must-reads* in certain research domains.
 
 ## Background
 
@@ -19,7 +19,7 @@ To get the news for newly released papers everyday, follow my [twitter](https://
 ## Awesome list criteria
 
 1. A list of **top 100 deep learning papers** published from 2012 to 2016 is suggested.
-2. If a paper is added to the list, another paper (usually from *More Papers from 2016" section) should be removed to keep top 100 papers. (Thus, removing papers is also important contributions as well as adding papers)
+2. If a paper is added to the list, another paper (usually from *More Papers from 2016" section) should be removed to keep the top 100 papers. (Thus, removing papers is also important contributions as well as adding papers)
 3. Papers that are important, but failed to be included in the list, will be listed in *More than Top 100* section.
 4. Please refer to *New Papers* and *Old Papers* sections for the papers published in recent 6 months or before 2012.
 
@@ -82,7 +82,7 @@ If you have any suggestions (missing papers, new papers, key researchers or typo
 ### Optimization / Training Techniques
 - **Training very deep networks** (2015), R. Srivastava et al. [[pdf]](http://papers.nips.cc/paper/5850-training-very-deep-networks.pdf)
 - **Batch normalization: Accelerating deep network training by reducing internal covariate shift** (2015), S. Loffe and C. Szegedy [[pdf]](http://arxiv.org/pdf/1502.03167)
-- **Delving deep into rectifiers: Surpassing human-level performance on imagenet classification** (2015), K. He et al. [[pdf]](http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf)
+- **Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification** (2015), K. He et al. [[pdf]](http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf)
 - **Dropout: A simple way to prevent neural networks from overfitting** (2014), N. Srivastava et al. [[pdf]](http://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf)
 - **Adam: A method for stochastic optimization** (2014), D. Kingma and J. Ba [[pdf]](http://arxiv.org/pdf/1412.6980)
 - **Improving neural networks by preventing co-adaptation of feature detectors** (2012), G. Hinton et al. [[pdf]](http://arxiv.org/pdf/1207.0580.pdf)
@@ -102,7 +102,7 @@ If you have any suggestions (missing papers, new papers, key researchers or typo
 <!---[Key researchers] [Yoshua Bengio](https://scholar.google.ca/citations?user=kukA0LcAAAAJ), [Ian Goodfellow](https://scholar.google.ca/citations?user=iYN86KEAAAAJ), [Alex Graves](https://scholar.google.ca/citations?user=DaFHynwAAAAJ)-->
 ### Convolutional Neural Network Models
 - **Rethinking the inception architecture for computer vision** (2016), C. Szegedy et al. [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Szegedy_Rethinking_the_Inception_CVPR_2016_paper.pdf)
-- **Inception-v4, inception-resnet and the impact of residual connections on learning** (2016), C. Szegedy et al. [[pdf]](http://arxiv.org/pdf/1602.07261)
+- **Inception-v4, inception-ResNet and the impact of residual connections on learning** (2016), C. Szegedy et al. [[pdf]](http://arxiv.org/pdf/1602.07261)
 - **Identity Mappings in Deep Residual Networks** (2016), K. He et al. [[pdf]](https://arxiv.org/pdf/1603.05027v2.pdf)
 - **Deep residual learning for image recognition** (2016), K. He et al. [[pdf]](http://arxiv.org/pdf/1512.03385)
 - **Spatial transformer network** (2015), M. Jaderberg et al., [[pdf]](http://papers.nips.cc/paper/5854-spatial-transformer-networks.pdf)
@@ -212,6 +212,8 @@ If you have any suggestions (missing papers, new papers, key researchers or typo
 
 ### New papers
 *Newly published papers (< 6 months) which are worth reading*
+- Improving Image Captioning with Conditional Generative Adversarial Nets (2019),Chen Chen et al[[pdf]](https://www.aaai.org/ojs/index.php/AAAI/article/view/4823)
+- Understanding Deep Neural Networks with Rectified Linear Units(2018),Raman Arora et al[[pdf]](https://arxiv.org/pdf/1611.01491.pdf)
 - MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (2017), Andrew G. Howard et al. [[pdf]](https://arxiv.org/pdf/1704.04861.pdf)
 - Convolutional Sequence to Sequence Learning (2017), Jonas Gehring et al. [[pdf]](https://arxiv.org/pdf/1705.03122)
 - A Knowledge-Grounded Neural Conversation Model (2017), Marjan Ghazvininejad et al. [[pdf]](https://arxiv.org/pdf/1702.01932)
@@ -321,7 +323,7 @@ If you have any suggestions (missing papers, new papers, key researchers or typo
 - What makes for effective detection proposals? (2016), J. Hosang et al. [[pdf]](https://arxiv.org/pdf/1502.05082)
 - Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks (2016), S. Bell et al. [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Bell_Inside-Outside_Net_Detecting_CVPR_2016_paper.pdf).
 - Instance-aware semantic segmentation via multi-task network cascades (2016), J. Dai et al. [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Dai_Instance-Aware_Semantic_Segmentation_CVPR_2016_paper.pdf)
-- Conditional image generation with pixelcnn decoders (2016), A. van den Oord et al. [[pdf]](http://papers.nips.cc/paper/6527-tree-structured-reinforcement-learning-for-sequential-object-localization.pdf)
+- Conditional image generation with PixelCNN decoders (2016), A. van den Oord et al. [[pdf]](http://papers.nips.cc/paper/6527-tree-structured-reinforcement-learning-for-sequential-object-localization.pdf)
 - Deep networks with stochastic depth (2016), G. Huang et al., [[pdf]](https://arxiv.org/pdf/1603.09382)
 - Consistency and Fluctuations For Stochastic Gradient Langevin Dynamics (2016), Yee Whye Teh et al. [[pdf]](http://www.jmlr.org/papers/volume17/teh16a/teh16a.pdf)
 
@@ -339,7 +341,7 @@ If you have any suggestions (missing papers, new papers, key researchers or typo
 - Character-aware neural language models (2015), Y. Kim et al. [[pdf]](https://arxiv.org/pdf/1508.06615)
 - Grammar as a foreign language (2015), O. Vinyals et al. [[pdf]](http://papers.nips.cc/paper/5635-grammar-as-a-foreign-language.pdf)
 - Trust Region Policy Optimization (2015), J. Schulman et al. [[pdf]](http://www.jmlr.org/proceedings/papers/v37/schulman15.pdf)
-- Beyond short snippents: Deep networks for video classification (2015) [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Ng_Beyond_Short_Snippets_2015_CVPR_paper.pdf)
+- Beyond short snippets: Deep networks for video classification (2015) [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Ng_Beyond_Short_Snippets_2015_CVPR_paper.pdf)
 - Learning Deconvolution Network for Semantic Segmentation (2015), H. Noh et al. [[pdf]](https://arxiv.org/pdf/1505.04366v1)
 - Learning spatiotemporal features with 3d convolutional networks (2015), D. Tran et al. [[pdf]](http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Tran_Learning_Spatiotemporal_Features_ICCV_2015_paper.pdf)
 - Understanding neural networks through deep visualization (2015), J. Yosinski et al. [[pdf]](https://arxiv.org/pdf/1506.06579)