Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.
Awesome Lists | Featured Topics | Projects
https://github.com/leo-p/papers

Papers and their summary (in issue)
https://github.com/leo-p/papers
Last synced: 9 days ago
JSON representation
Papers and their summary (in issue)
Host: GitHub
URL: https://github.com/leo-p/papers
Owner: leo-p
Created: 2017-05-22T17:46:00.000Z (over 7 years ago)
Default Branch: master
Last Pushed: 2017-07-04T14:10:48.000Z (over 7 years ago)
Last Synced: 2024-08-02T15:25:52.307Z (3 months ago)
Size: 7.81 KB
Stars: 22
Watchers: 3
Forks: 4
Open Issues: 39
Metadata Files:
- Readme: README.md
Awesome Lists containing this project

README

        # Papers read so far

- 17.06 - **Self-Normalizing Neural Networks**

    - [pdf](https://arxiv.org/pdf/1706.02515), [summary](https://github.com/leo-p/papers/issues/31)

- 17.06 - **StreetStyle: Exploring world-wide clothing styles from millions of photos**

    - [pdf](https://arxiv.org/pdf/1706.01869), [summary](https://github.com/leo-p/papers/issues/35) 

- 17.05 - **Visual Attribute Transfer through Deep Image Analogy**

    - [pdf](https://arxiv.org/pdf/1705.01088), [summary](https://github.com/leo-p/papers/issues/24) 

- 17.05 - **pix2code: Generating Code from a Graphical User Interface Screenshot**

    - [pdf](https://arxiv.org/pdf/1705.07962), [summary](https://github.com/leo-p/papers/issues/36)

- 17.05 - **A System for Accessible Artificial Intelligence**

    - [pdf](https://arxiv.org/pdf/1705.00594), [summary](https://github.com/leo-p/papers/issues/39)

- 17.05 - **Domain Adaptation with Randomized Multilinear Adversarial Networks**

    - [pdf](https://arxiv.org/pdf/1705.10667), [summary](https://github.com/leo-p/papers/issues/29)

- 17.04 - **Softmax GAN**

    - [pdf](https://arxiv.org/pdf/1704.06191), [summary](https://github.com/leo-p/papers/issues/23)

- 17.04 - **Generate To Adapt - Aligning Domains using Generative Adversarial Networks**

    - [pdf](https://arxiv.org/pdf/1704.01705v1), [summary](https://github.com/leo-p/papers/issues/22)

- 17.03 - **Neural Episodic Control**

    - [pdf](https://arxiv.org/pdf/1703.01988), [summary](https://github.com/leo-p/papers/issues/21)

- 17.03 - **Mask R-CNN**

    - [pdf](https://arxiv.org/pdf/1703.06870), [summary](https://github.com/leo-p/papers/issues/20)

- 17.03 - **BEGAN- Boundary Equilibrium Generative Adversarial Networks**

    - [pdf](https://arxiv.org/pdf/1703.10717v3), [summary](https://github.com/leo-p/papers/issues/19)

- 17.03 - **Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks**

    - [pdf](https://arxiv.org/pdf/1703.10593), [summary](https://github.com/leo-p/papers/issues/30)

- 17.02 - **Understanding Deep Learning Requires Rethinking Generalization**

    - [pdf](https://arxiv.org/pdf/1611.03530), [summary](https://github.com/leo-p/papers/issues/18)

- 17.02 - **Adversarial Discriminative Domain Adaptation**

    - [pdf](https://arxiv.org/pdf/1702.05464), [summary](https://github.com/leo-p/papers/issues/17)

- 17.01 - **Cost-Effective Active Learning for Deep Image Classification**

    - [pdf](https://arxiv.org/pdf/1701.03551), [summary](https://github.com/leo-p/papers/issues/40)

- 17.01 - **Wasserstein GAN**

    - [pdf](https://arxiv.org/pdf/1701.07875), [summary](https://github.com/leo-p/papers/issues/16)

- 17.01 - **NIPS Tutorial - Generative Adversarial Networks**

    - [pdf](https://arxiv.org/pdf/1701.00160), [summary](https://github.com/leo-p/papers/issues/15)

- 17.01 - **Learning From Noisy Large-Scale Datasets With Minimal Supervision**

    - [pdf](https://arxiv.org/pdf/1701.01619), [summary](https://github.com/leo-p/papers/issues/14)

- 16.12 - **Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space**

    - [pdf](https://arxiv.org/pdf/1612.00005v2), [summary](https://github.com/leo-p/papers/issues/28)

- 16.12 - **YOLO9000: Better, Faster, Stronger**

    - [pdf](https://arxiv.org/pdf/1612.08242), [summary](https://github.com/leo-p/papers/issues/13)

- 16.11 - **Deep Information Propagation**

    - [pdf](https://arxiv.org/pdf/1611.01232), [summary](https://github.com/leo-p/papers/issues/12)

- 16.11 - **Speed/accuracy trade-offs for modern convolutional object detectors**

    - [pdf](https://arxiv.org/pdf/1611.10012), [summary](https://github.com/leo-p/papers/issues/37)

- 16/11 - **Neural Architecture Search with Reinforcement Learning**

    - [pdf](https://arxiv.org/pdf/1611.01578), [summary](https://github.com/leo-p/papers/issues/26)

- 16.07 - **Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation**

    - [pdf](https://arxiv.org/pdf/1607.03516), [summary](https://github.com/leo-p/papers/issues/11)

- 16.05 - **Domain-Adversarial Training of Neural Networks**

    - [pdf](https://arxiv.org/pdf/1505.07818), [summary](https://github.com/leo-p/papers/issues/6)

- 16.01 - **Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks**

    - [pdf](https://arxiv.org/pdf/1506.01497), [summary](https://github.com/leo-p/papers/issues/9)

- 15.12 - **Deep Residual Learning for Image Recognition**

    - [pdf](https://arxiv.org/pdf/1512.03385), [summary](https://github.com/leo-p/papers/issues/27)

- 15.11 - **Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks**

    - [pdf](https://arxiv.org/pdf/1511.06434), [summary](https://github.com/leo-p/papers/issues/8)

- 15.04 - **Active Learning by Learning**

    - [pdf](https://www.csie.ntu.edu.tw/~htlin/paper/doc/aaai15albl.pdf), [summary](https://github.com/leo-p/papers/issues/34)

- 15.04 - **Fast R-CNN**

    - [pdf](https://arxiv.org/pdf/1504.08083), [summary](https://github.com/leo-p/papers/issues/7)

- 15.02 - **Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift**

    - [pdf](https://arxiv.org/pdf/1502.03167), [summary](https://github.com/leo-p/papers/issues/5)

- 14.12 - **Dropout: A Simple Way to Prevent Neural Networks from Overfitting**

    - [pdf](https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf), [summary](test)

- 14.12 - **Training Deep Neural Networks on Noisy Labels with Bootstrapping**

    - [pdf](https://arxiv.org/pdf/1412.6596), [summary](https://github.com/leo-p/papers/issues/38)

- 14.11 - **Conditional Generative Adversarial Nets**

    - [pdf](https://arxiv.org/pdf/1411.1784), [summary](https://github.com/leo-p/papers/issues/4)

- 14.09 - **Unsupervised Domain Adaptation by Backpropagation**

    - [pdf](https://arxiv.org/pdf/1409.7495), [summary](https://github.com/leo-p/papers/issues/3)

- 14.06 - **Generative Adversarial Nets**

    - [pdf](https://arxiv.org/pdf/1406.2661), [summary](https://github.com/leo-p/papers/issues/2)  

- 14.06 - **Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks**

    - [pdf](https://www.di.ens.fr/willow/pdfscurrent/oquab14cvpr.pdf), [summary](https://github.com/leo-p/papers/issues/32)

- 11.06 - **Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction**

    - [pdf](https://pdfs.semanticscholar.org/1c6d/990c80e60aa0b0059415444cdf94b3574f0f.pdf), [summary](https://github.com/leo-p/papers/issues/25)

- 10.06 - **Deconvolutional Networks**

    - [pdf](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf), [summary](https://github.com/leo-p/papers/issues/1)

- 10.01 - **Active Learning Literature Survey**

    - [pdf](http://burrsettles.com/pub/settles.activelearning.pdf), [summary](https://github.com/leo-p/papers/issues/33)