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https://github.com/chahatdeep/awesome-papers

A list of recent papers regarding deep learning, reinforcement learning, GANs.
https://github.com/chahatdeep/awesome-papers

List: awesome-papers

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A list of recent papers regarding deep learning, reinforcement learning, GANs.

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# Awesome-Papers



[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)



### [Compression](Compressing-NN.md)

### [GitHub Implementations](Github-Implementations.md)

+ [x] [handong-github](https://github.com/handong1587/handong1587.github.io/tree/e5dfaff2898076b7a1a5a0d28a9ec9a0cd182c68/_posts/deep_learning)



***



A list of recent papers regarding deep learning, reinforcement learning, GANs and Quadrotor Control Theory.



## Latest:

- [x] [GMS: Grid-based Motion Statistics for Fast, Ultra-robust Feature Correspondence](http://openaccess.thecvf.com/content_cvpr_2017/papers/Bian_GMS_Grid-based_Motion_CVPR_2017_paper.pdf) [Code: C++/Python/MATLAB](https://github.com/JiawangBian/GMS-Feature-Matcher)



## Deep Learning:

### 2017

1. [Learning with Opponent-Learning Awareness](https://arxiv.org/pdf/1709.04326.pdf). Jakob N. Foerster et. al. OpenAI, University of Oxford, UC Berkeley, CMU. 

2. [Towards Proving the Adversarial Robustness of Deep Neural Networks](https://arxiv.org/pdf/1709.02802.pdf). Guy Katz et. al. Standford University.

3. [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://arxiv.org/pdf/1706.02677.pdf). Priya Goyal et al. Facebook.

4. [Continuous-Time Flows for Deep Generative Models](https://arxiv.org/pdf/1709.01179.pdf). Changyou Chen et al. University of Buffalo, Duke University.

5. [Deep Learning Techniques for Music Generation: A Survey](https://arxiv.org/pdf/1709.01620.pdf). Jean-Pierre Briot et al. Ecole Poly. Sony etc.

6. [Learning Graph Topological Features via GAN](https://arxiv.org/pdf/1709.03545.pdf). Weiyi Liu et al. University of Electronic Science and Technology of China, IBM Watson Research Center, Columbia University, Boston University.

7. [Label Denoising Adversarial Network (LDAN) for Inverse Lighting of Face Images](https://arxiv.org/pdf/1709.01993.pdf) David Jacobs. University of Maryland- College Park, MD.

8. [CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training](https://arxiv.org/pdf/1709.02023.pdf). UT Austin.

9. **[Generative Adversarial Nets](https://arxiv.org/pdf/1406.2661.pdf) Goodfellow et. al. University de Montreal. (2014).**

10. ** [ProjectionNet: Learning Efficient On-Device Deep Networks Using Neural Projections](https://arxiv.org/pdf/1708.00630.pdf) Sujith Ravi. Google Research. **

11. [How to Train A GAN?](https://github.com/soumith/ganhacks). Soumith.

12. [A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation](https://arxiv.org/pdf/1707.01202.pdf). Vishwanath et al. Rutgers.

13. [Recent Advances in Convolutional Neural Networks](https://arxiv.org/pdf/1512.07108.pdf). Jiuxiang Gu. NTU, Singapore.

14. [Neural Style Transfer: A Review](https://arxiv.org/pdf/1705.04058.pdf). Yongcheng Jing. Microsoft, Arizona State University.

15. [SIFT Meets CNN: A Decade Survey of Instance Retrieval](https://arxiv.org/pdf/1608.01807.pdf). Liang Zheng et al. UTS, Australia.

16. [Survey on the attention based RNN model and its applications in computer vision](https://arxiv.org/pdf/1601.06823.pdf). Feng Wang. Delft.

17. [Geometric GAN](https://arxiv.org/pdf/1705.02894.pdf). Jae Hyun Lim et al. KAIST. May 2017.

18. [Depth Estimation from Single Image Using CNN-Residual Network](http://cs231n.stanford.edu/reports/2017/pdfs/203.pdf). Xiaobai Ma, Standford. (2017)

19. [Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space](https://arxiv.org/pdf/1612.00005.pdf). Yoshua Bengio et al. (April 2017)

20. **[Wasserstein GAN](https://arxiv.org/pdf/1701.07875.pdf). Martin Arjovsky et al. Courant Institute of Mathematical Sciences, Facebook AI Research. March 2017. [Code](https://github.com/fairytale0011/Conditional-WassersteinGAN)**



### 2016:

1. [A Taxonomy of Deep Convolutional Neural Nets for Computer Vision](https://arxiv.org/pdf/1601.06615.pdf). Suraj Srinivas et al. IISc Bangalore, 2016.

2. **[Coupled Generative Adversarial Networks](https://arxiv.org/pdf/1606.07536.pdf). Ming-Yu Liu et al. Mitsubishi Electric Research Labs (MERL, 2016).**

3. **[Semantic Segmentation using Adversarial Networks](https://arxiv.org/pdf/1611.08408.pdf). Pauline Luc. Facebook AI Research. (2016)**

4. **[Improved Techniques for Training GANs](https://arxiv.org/pdf/1606.03498.pdf). Ian Goodfellow. OpenAI. [Code](https://github.com/openai/improved-gan). (2016).**



### 2015:

1. [Compressing Neural Networks with the Hashing Trick](https://arxiv.org/pdf/1504.04788.pdf). Wenlin Chen et al. Nvidia, UWash St. Louis. (2015).



***



## Quadrotor Controls:

1. [Design of Decoupling and Nonlinear PD Controller for Cruise Control of a Quadrotor](https://arxiv.org/pdf/1708.04584.pdf). Aug 2017.

2. [Modelling and control of quadcopter](http://sal.aalto.fi/publications/pdf-files/eluu11_public.pdf), Teppo Luukkonen. (2011)

3. [Aggressive Quadrotor Flight through Narrow Gaps with Onboard Sensing and Computing using Active Vision](http://rpg.ifi.uzh.ch/doczercs/ICRA17_Falanga.pdf), Falanga, David Scaramuzza et al, (ICRA 2017). [Supplementary video](http://rpg.ifi.uzh.ch/aggressive_flight.html)

4. N. Michael, D. Mellinger, Q. Lindsey, and V. Kumar, “The GRASP multiple micro UAV testbed,” IEEE Robot. Autom. Mag., vol. 17, no. 3, pp. 56–65, Sep. 2010.

5. M. Faessler, F. Fontana, C. Forster, and D. Scaramuzza, “Automatic reinitialization and failure recovery for aggressive flight with a monocular vision-based quadrotor,” in Proc. IEEE Int. Conf. Robot. Autom., 2015, pp. 1722–1729.

6.  D. Mellinger, N. Michael, and V. Kumar, “Trajectory generation and control for precise aggressive maneuvers with quadrotors,” Int. J. Robot. Res., vol. 31, no. 5, pp. 664–674, 2012.

7. M. Hehn and R. D’Andrea, “A frequency domain iterative learning algorithm for high-performance, periodic quadrocopter maneuvers,” J. Mechatronics, vol. 24, no. 8, pp. 954–965, 2014.

8. 



### LQR Controller:

1. [PID, LQR and LQR-PID on a quadcopter platform](http://ieeexplore.ieee.org/document/6572698/), Lucas M. Argentim et al. (2013)

2. [Multi-Agent Testbed development, modelling and control of Quadrotor UAVs](http://kth.diva-portal.org/smash/get/diva2:551115/FULLTEXT01.pdf). p.p. 27-31, KTH Thesis. (2012)

3. [Comparison of PID and LQR controllers on a quadrotor helicopter](http://www.naun.org/main/UPress/saed/2015/a442014-074.pdf), Demet Canpolat Tosun et al. (2015)

4. [LQR- MIT Reference, A Good Theoritical Proof](https://ocw.mit.edu/courses/mechanical-engineering/2-154-maneuvering-and-control-of-surface-and-underwater-vehicles-13-49-fall-2004/lecture-notes/lec19.pdf), MIT. 

###### Murray References(Caltech):

- [LQR- Caltech Reference, A quick Guide to LQR](https://www.cds.caltech.edu/~murray/courses/cds110/wi06/lqr.pdf), Caltech (Murray), Control and Dynamical Systems (CDS 110b)

- [Linear Quadratic Regulators: Proof (Slides)](http://www.cds.caltech.edu/~murray/courses/cds110/wi08/L3-1_lqr.pdf)

- [Optimal Control](http://www.cds.caltech.edu/~murray/courses/cds110/wi08/L2-1_optimal.pdf)

- **[Optimal Control Theory: LQR](http://www.cds.caltech.edu/~murray/courses/cds110/wi08/optimal_14Jan08.pdf)**

- [Murray Website Reference](http://www.cds.caltech.edu/~murray/wiki/index.php/CDS_110b:_Optimal_Control)

- [A Review of Online Control Customization via Optimization-Based Control](http://www.cds.caltech.edu/~murray/preprints/mur+03-sec.pdf)



[LQR Inverted Pendulum Simulator](https://github.com/SwapUNaph/LQR-control-model)



#### Quadrotor Hacks:

1. [Oscar Liang: Tuning a PID](https://oscarliang.com/quadcopter-pid-explained-tuning/)

2. [HOW TO TUNE A QUADCOPTER PID LOOP: THE SIMPLE WAY](https://myfirstdrone.com/tutorials/how-to-tune-a-quadcopter/)



#### Quadrotor Simulators:

1. [LQR Quadrotor MATLAB](https://github.com/aarkebauer/QuadrotorLQR)