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Added RetinaNet paper and a few empty lines #71

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12 changes: 12 additions & 0 deletions README.md
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Expand Up @@ -22,6 +22,7 @@ To get the news for newly released papers everyday, follow my [twitter](https://
4. Please refer to *New Papers* and *Old Papers* sections for the papers published in recent 6 months or before 2012.

*(Citation criteria)*

- **< 6 months** : *New Papers* (by discussion)
- **2016** : +60 citations or "More Papers from 2016"
- **2015** : +200 citations
Expand Down Expand Up @@ -210,6 +211,8 @@ If you have any suggestions (missing papers, new papers, key researchers or typo

### New papers
*Newly published papers (< 6 months) which are worth reading*

- Focal Loss for Dense Object Detection (2017), T.-Y. Lin et al. [[pdf]](https://arxiv.org/abs/1708.02002)
- 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)
Expand All @@ -230,6 +233,7 @@ If you have any suggestions (missing papers, new papers, key researchers or typo

### Old Papers
*Classic papers published before 2012*

- An analysis of single-layer networks in unsupervised feature learning (2011), A. Coates et al. [[pdf]](http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2011_CoatesNL11.pdf)
- Deep sparse rectifier neural networks (2011), X. Glorot et al. [[pdf]](http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2011_GlorotBB11.pdf)
- Natural language processing (almost) from scratch (2011), R. Collobert et al. [[pdf]](http://arxiv.org/pdf/1103.0398)
Expand All @@ -249,6 +253,7 @@ If you have any suggestions (missing papers, new papers, key researchers or typo


### HW / SW / Dataset

- SQuAD: 100,000+ Questions for Machine Comprehension of Text (2016), Rajpurkar et al. [[pdf]](https://arxiv.org/pdf/1606.05250.pdf)
- OpenAI gym (2016), G. Brockman et al. [[pdf]](https://arxiv.org/pdf/1606.01540)
- TensorFlow: Large-scale machine learning on heterogeneous distributed systems (2016), M. Abadi et al. [[pdf]](http://arxiv.org/pdf/1603.04467)
Expand All @@ -260,6 +265,7 @@ If you have any suggestions (missing papers, new papers, key researchers or typo


### Book / Survey / Review

- On the Origin of Deep Learning (2017), H. Wang and Bhiksha Raj. [[pdf]](https://arxiv.org/pdf/1702.07800)
- Deep Reinforcement Learning: An Overview (2017), Y. Li, [[pdf]](http://arxiv.org/pdf/1701.07274v2.pdf)
- Neural Machine Translation and Sequence-to-sequence Models(2017): A Tutorial, G. Neubig. [[pdf]](http://arxiv.org/pdf/1703.01619v1.pdf)
Expand All @@ -274,18 +280,21 @@ If you have any suggestions (missing papers, new papers, key researchers or typo
### Video Lectures / Tutorials / Blogs

*(Lectures)*

- CS231n, Convolutional Neural Networks for Visual Recognition, Stanford University [[web]](http://cs231n.stanford.edu/)
- CS224d, Deep Learning for Natural Language Processing, Stanford University [[web]](http://cs224d.stanford.edu/)
- Oxford Deep NLP 2017, Deep Learning for Natural Language Processing, University of Oxford [[web]](https://github.com/oxford-cs-deepnlp-2017/lectures)

*(Tutorials)*

- NIPS 2016 Tutorials, Long Beach [[web]](https://nips.cc/Conferences/2016/Schedule?type=Tutorial)
- ICML 2016 Tutorials, New York City [[web]](http://techtalks.tv/icml/2016/tutorials/)
- ICLR 2016 Videos, San Juan [[web]](http://videolectures.net/iclr2016_san_juan/)
- Deep Learning Summer School 2016, Montreal [[web]](http://videolectures.net/deeplearning2016_montreal/)
- Bay Area Deep Learning School 2016, Stanford [[web]](https://www.bayareadlschool.org/)

*(Blogs)*

- OpenAI [[web]](https://www.openai.com/)
- Distill [[web]](http://distill.pub/)
- Andrej Karpathy Blog [[web]](http://karpathy.github.io/)
Expand All @@ -296,6 +305,7 @@ If you have any suggestions (missing papers, new papers, key researchers or typo

### Appendix: More than Top 100
*(2016)*

- A character-level decoder without explicit segmentation for neural machine translation (2016), J. Chung et al. [[pdf]](https://arxiv.org/pdf/1603.06147)
- Dermatologist-level classification of skin cancer with deep neural networks (2017), A. Esteva et al. [[html]](http://www.nature.com/nature/journal/v542/n7639/full/nature21056.html)
- Weakly supervised object localization with multi-fold multiple instance learning (2017), R. Gokberk et al. [[pdf]](https://arxiv.org/pdf/1503.00949)
Expand Down Expand Up @@ -324,6 +334,7 @@ If you have any suggestions (missing papers, new papers, key researchers or typo
- Consistency and Fluctuations For Stochastic Gradient Langevin Dynamics (2016), Yee Whye Teh et al. [[pdf]](http://www.jmlr.org/papers/volume17/teh16a/teh16a.pdf)

*(2015)*

- Ask your neurons: A neural-based approach to answering questions about images (2015), M. Malinowski et al. [[pdf]](http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Malinowski_Ask_Your_Neurons_ICCV_2015_paper.pdf)
- Exploring models and data for image question answering (2015), M. Ren et al. [[pdf]](http://papers.nips.cc/paper/5640-stochastic-variational-inference-for-hidden-markov-models.pdf)
- Are you talking to a machine? dataset and methods for multilingual image question (2015), H. Gao et al. [[pdf]](http://papers.nips.cc/paper/5641-are-you-talking-to-a-machine-dataset-and-methods-for-multilingual-image-question.pdf)
Expand Down Expand Up @@ -358,6 +369,7 @@ If you have any suggestions (missing papers, new papers, key researchers or typo


*(~2014)*

- DeepPose: Human pose estimation via deep neural networks (2014), A. Toshev and C. Szegedy [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Toshev_DeepPose_Human_Pose_2014_CVPR_paper.pdf)
- Learning a Deep Convolutional Network for Image Super-Resolution (2014, C. Dong et al. [[pdf]](https://www.researchgate.net/profile/Chen_Change_Loy/publication/264552416_Lecture_Notes_in_Computer_Science/links/53e583e50cf25d674e9c280e.pdf)
- Recurrent models of visual attention (2014), V. Mnih et al. [[pdf]](http://arxiv.org/pdf/1406.6247.pdf)
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