hdDeepLearningStudy

Papers,code etc for deep learning study group
#Suggestions for future readings
https://arxiv.org/pdf/1605.06431v1.pdf - Deep nets are ensembles
https://arxiv.org/pdf/1602.08124v3.pdf - soa for parallelization
https://arxiv.org/pdf/1404.5997v2.pdf - parallel computation issues
http://www.wsdm-conference.org/2016/slides/WSDM2016-Jeff-Dean.pdf - distributed architecture
https://www.youtube.com/watch?v=sUzQpd-Ku4o - video of jeff dean talk
https://arxiv.org/pdf/1611.01578v1.pdf - RL for finding neural architectures
http://mlg.eng.cam.ac.uk/yarin/blog_2248.html - uncertainty in neural nets
https://arxiv.org/pdf/1611.01587.pdf - Joint Many-task model: Neural Net for multiple NLP Tasks - Socher
http://papers.nips.cc/paper/5773-deep-generative-image-models-using-a-laplacian-pyramid-of-adversarial-networks.pdf -GAN paper (recc by LeCun)
https://arxiv.org/pdf/1511.05440.pdf - GAN for video prediction
https://arxiv.org/abs/1703.02528 - Generative unadversarial networks
https://arxiv.org/pdf/1611.01578.pdf - Neural architecture search with RL - google brain
https://arxiv.org/pdf/1703.01041.pdf - Large-Scale Evolution of Image Classifiers - google brain

Apr 10 - Hacker Dojo

https://arxiv.org/pdf/1603.08678.pdf - Instance-sensitive Fully Convolutional Networks

https://arxiv.org/pdf/1611.07709.pdf - Fully Convolutional Instance-aware Semantic Segmentation

Apr 3 - Hacker Dojo

https://arxiv.org/pdf/1703.03864.pdf - Sutskever paper on using evolutionary systems for optimizing RL prob
http://jmlr.csail.mit.edu/papers/volume15/wierstra14a/wierstra14a.pdf - ES paper with algo used in Sutskever paper

Mar 27 - Hacker Dojo

Aurobindo Tripathy will reprise a talk he's going to give at Embedded Summit this year. His talk will survey recent progress in object detection from RCNN to Single Shot MultiBox Detector and Yolo 9000.

Mar 20 - Hacker Dojo

https://arxiv.org/pdf/1612.05424.pdf - Unsupervised Pixel-level domain adaptation with generative adversarial networks

Mar 13 - Hacker Dojo

https://arxiv.org/pdf/1701.06547.pdf - adversarial learning for neural dialog generation

February 27 - Hacker Dojo

https://arxiv.org/pdf/1612.02699.pdf - Deep Supervision with Shape Concepts for Occlusion-Aware 3D Object Parsing
Zeeshan's slides are in the folder with his name on it. Along with his descriptions of his own ground-breaking work, he gives an excellent history of efforts to identify 3d objects from 2d images.

February 20 - Hacker Dojo

https://arxiv.org/pdf/1506.07285.pdf - Ask me anything - Socher
https://github.com/YerevaNN/Dynamic-memory-networks-in-Theano - Code and implementation notes.
https://www.youtube.com/watch?v=FCtpHt6JEI8&t=27s - Socher presentation of material

February 13 - Hacker Dojo

https://arxiv.org/pdf/1701.06538v1.pdf - Outrageously large neural networks

February 6 - Hacker Dojo

https://arxiv.org/pdf/1505.00387v2.pdf - Highway networks
https://arxiv.org/pdf/1507.06228.pdf - Also highway networks - different examples
https://arxiv.org/pdf/1607.03474v3.pdf - Recurrent Highway Networks

January 30 - Hacker Dojo

https://arxiv.org/pdf/1603.03116v2.pdf - Low-rank pass-through RNN's follow-on to unitary rnn https://github.com/Avmb/lowrank-gru - theano code

January 23 - HackerDojo

https://arxiv.org/abs/1612.03242 - Stack Gan Paper
https://github.com/hanzhanggit/StackGAN - Code

January 16 - Hacker Dojo

https://arxiv.org/pdf/1511.06464v4.pdf - Unitary Evolution RNN https://github.com/amarshah/complex_RNN - theano code

January 9 - Hacker Dojo

Cheuksan Edward Wang Talk
https://arxiv.org/pdf/1612.04642v1.pdf - rotation invariant cnn
https://github.com/deworrall92/harmonicConvolutions - tf code for harmonic cnn http://visual.cs.ucl.ac.uk/pubs/harmonicNets/index.html - blog post by authors

January 2 - Hacker Dojo

https://arxiv.org/pdf/1602.02218v2.pdf - using typing to improve RNN behavior
http://jmlr.org/proceedings/papers/v37/jozefowicz15.pdf - exploration of alternative LSTM architectures

December 19 - Hacker Dojo

https://arxiv.org/pdf/1611.01576.pdf - Socher qRnn paper

December 12 - Hacker Dojo

https://arxiv.org/pdf/1604.02135v2.pdf - latest segmentation fair
https://github.com/MarvinTeichmann/tensorflow-fcn - code for segmenter

December 5 - Hacker Dojo

https://arxiv.org/pdf/1506.06204.pdf - Object segmentation https://arxiv.org/pdf/1603.08695v2.pdf - refinement of above segmentation paper
https://code.facebook.com/posts/561187904071636/segmenting-and-refining-images-with-sharpmask/ - blog post
https://github.com/facebookresearch/deepmask - torch code for deepmask

November 28 - Hacker Dojo

https://arxiv.org/pdf/1506.01497v3.pdf
people.eecs.berkeley.edu/~rbg/slides/rbg-defense-slides.pdf - Girshick thesis slides
Check edge boxes and selective search
https://arxiv.org/pdf/1406.4729v4.pdf - key part of architecture
https://github.com/smallcorgi/Faster-RCNN_TF - excellent code

November 21 - Hacker Dojo

https://people.eecs.berkeley.edu/~rbg/papers/r-cnn-cvpr.pdf - RCNN
https://arxiv.org/pdf/1504.08083v2.pdf - RCNN - first in series
https://arxiv.org/pdf/1506.01497v3.pdf - Faster R-CNN
http://techtalks.tv/talks/rich-feature-hierarchies-for-accurate-object-detection-and-semantic-segmentation/60254/ - video of Girshick talk

November 14 - Hacker Dojo

https://arxiv.org/pdf/1506.02025v3.pdf - Spatial transformer networks
https://github.com/daviddao/spatial-transformer-tensorflow - tf code for above

October 31 - Hacker Dojo

https://github.com/jazzsaxmafia/show_attend_and_tell.tensorflow - tf code for attention-captioning http://cs.stanford.edu/people/karpathy/densecap/ - karpathy captioning https://arxiv.org/pdf/1412.2306v2.pdf - earlier karpathy captioning paper

October 20 - Galvanize

https://webdocs.cs.ualberta.ca/~sutton/book/the-book.html - Deep dive into reinforcement learning - Sutton and Barto - Chapters 1 and 2.

Oct 17 - Hacker Dojo

https://arxiv.org/pdf/1608.06993v1.pdf - DenseNet. New reigning champion image classifier
https://github.com/liuzhuang13/DenseNet - lua code
The DenseNet paper is straight-forward, so we're also going to start on image captioning

http://www.cs.toronto.edu/~zemel/documents/captionAttn.pdf
http://kelvinxu.github.io/projects/capgen.html
http://people.ee.duke.edu/~lcarin/Yunchen9.25.2015.pdf - slides for caption attention

collections of captioning papers. https://github.com/kjw0612/awesome-deep-vision#image-captioning - images
https://github.com/kjw0612/awesome-deep-vision#video-captioning - video

Oct 13 - SF

http://www.mit.edu/~dimitrib/NDP_Encycl.pdf - (early) Bersekas paper on RL, policy and value iteration
http://www.nervanasys.com/demystifying-deep-reinforcement-learning/?imm_mid=0e2d7e&cmp=em-data-na-na-newsltr_20160420 - blog post on RL. Nice coverage of value iteration

Oct 10 - Hacker Dojo

https://github.com/carpedm20/pixel-rnn-tensorflow - tensorflow code for pixel rnn (and cnn)

Sept 19 - Hacker Dojo

https://arxiv.org/pdf/1606.05328v2.pdf - Conditional Image Generation with PixelCNN decoders
https://arxiv.org/pdf/1601.06759v3.pdf - Pixel RNN
https://drive.google.com/file/d/0B3cxcnOkPx9AeWpLVXhkTDJINDQ/view - wavenet Generative Audio
https://deepmind.com/blog/wavenet-generative-model-raw-audio/ - wavenet blog

Sept 15 - Galvanize SF

http://www.gitxiv.com/posts/fepYG4STYaej3KSPZ/densely-connected-convolutional-netowork-densenet

Sept 12 - Hacker Dojo

http://arxiv.org/pdf/1410.3916v11.pdf - original memory networks
https://arxiv.org/pdf/1606.03126v1.pdf - key/value memory augmented nn http://www.thespermwhale.com/jaseweston/icml2016/icml2016-memnn-tutorial.pdf#page=87 - tutorial on memory networks in language understanding

August 29 - Hacker Dojo

https://arxiv.org/pdf/1410.5401v2.pdf - Neural Turing Machines
https://github.com/carpedm20/NTM-tensorflow
https://www.youtube.com/watch?v=_H0i0IhEO2g - Alex Graves presentation at microsoft research
http://www.robots.ox.ac.uk/~tvg/publications/talks/NeuralTuringMachines.pdf - slides for ntm

August 25 - Galvanize (SF)

http://arxiv.org/pdf/1410.3916v11.pdf - original memory networks
https://arxiv.org/pdf/1606.03126v1.pdf - key/value memory augmented nn http://www.thespermwhale.com/jaseweston/icml2016/icml2016-memnn-tutorial.pdf#page=87 - tutorial on memory networks in language understanding

August 22 - Hacker Dojo

https://arxiv.org/pdf/1605.07648v1.pdf - fractal net - alternative to resnet for ultra-deep convolution https://github.com/edgelord/FractalNet - tf code
http://www.gitxiv.com/posts/ibA8QEu8bvBJSDxr9/fractalnet-ultra-deep-neural-networks-without-residuals

August 18, 2016 - Galvanize (SF)

https://arxiv.org/pdf/1602.01783v2.pdf - new RL architecture - deep mind

Code: https://github.com/Zeta36/Asynchronous-Methods-for-Deep-Reinforcement-Learning - tf
https://github.com/miyosuda/async_deep_reinforce - tf
https://github.com/coreylynch/async-rl - keras (tf)
https://github.com/muupan/async-rl - chainer (good discussion)

August 15, 2016 - Hacker Dojo

https://arxiv.org/pdf/1607.02533v1.pdf - Hardening deep networks to adversarial examples.

August 11, 2016 - Galvanize (SF)

http://www.gitxiv.com/posts/HQJ3F9YzsQZ3eJjpZ/model-free-episodic-control - deep mind gitxiv paper and code on github https://github.com/sudeepraja/Model-Free-Episodic-Control - other code https://github.com/ShibiHe/Model-Free-Episodic-Control

August 8, 2016 - Hacker Dojo

https://arxiv.org/pdf/1406.2661.pdf - originating paper on generative adversarial net (gan) - goodfellow, bengio
http://arxiv.org/pdf/1511.06434v2.pdf - deep cnn gan - radford
https://github.com/Newmu/dcgan_code - theano code for cnn gan - radford

August 4, 2016 - Galvanize (SF)

http://www.gitxiv.com/posts/HQJ3F9YzsQZ3eJjpZ/model-free-episodic-control - deep mind gitxiv paper and code on github

August 1, 2016 - Hacker Dojo

Papers -
https://drive.google.com/file/d/0B8Dg3PBX90KNWG5KQXNQOFlBLU1JWWVONkN1UFpnbUR6Y0cw/view?pref=2&pli=1 - Using Stochastic RNN for temporal anomaly detection
https://home.zhaw.ch/~dueo/bbs/files/vae.pdf - cover math
https://arxiv.org/pdf/1401.4082v3.pdf - Rezende - Other Original VAE paper

Code Review -
https://github.com/oduerr/dl_tutorial/blob/master/tensorflow/vae/vae_demo.ipynb
https://github.com/oduerr/dl_tutorial/blob/master/tensorflow/vae/vae_demo-2D.ipynb

July 28, 2016 - SF

Papers:
http://arxiv.org/pdf/1410.5401v2.pdf - Neural Turing Machines - Graves et. al.
https://arxiv.org/pdf/1605.06065v1.pdf - One Shot Learning - DeepMind

Code:
http://icml.cc/2016/reviews/839.txt
https://github.com/brendenlake/omniglot
https://github.com/tristandeleu/ntm-one-shot
https://github.com/MLWave/extremely-simple-one-shot-learning

July 25, 2016 - Hacker Dojo

Papers - Using VAE for anomaly detection
https://arxiv.org/pdf/1411.7610.pdf - Stochastic Recurrent Networks
https://drive.google.com/file/d/0B8Dg3PBX90KNWG5KQXNQOFlBLU1JWWVONkN1UFpnbUR6Y0cw/view?pref=2&pli=1 - Using Stochastic RNN for temporal anomaly detection

July 21, 2016 - SF

Papers to read:
http://www.thespermwhale.com/jaseweston/ram/papers/paper_16.pdf
http://snowedin.net/tmp/Hochreiter2001.pdf -

Comments / Code
http://icml.cc/2016/reviews/839.txt
https://github.com/brendenlake/omniglot
https://github.com/tristandeleu/ntm-one-shot
https://github.com/MLWave/extremely-simple-one-shot-learning
https://www.periscope.tv/hugo_larochelle/1ypJdnPRYEoKW

July 18, 2016 - Hacker Dojo

Papers to read:
http://arxiv.org/pdf/1312.6114v10.pdf - variational autoencoders - U of Amsterdam - Kingma and Welling
http://arxiv.org/pdf/1310.8499v2.pdf - deep autoregressive networks - deep mind
https://arxiv.org/abs/1606.05908 - tutorial on vae

Commentaries/Code
https://jmetzen.github.io/2015-11-27/vae.html - metzen - code and discussion
http://blog.keras.io/building-autoencoders-in-keras.html - chollet - discusses different autoencoders, gives keras code.

June 27, July 11 2016 - Hacker Dojo

Recurrent network for image generation - Deep Mind
https://arxiv.org/pdf/1502.04623v2.pdf
Background and some references cited
http://blog.evjang.com/2016/06/understanding-and-implementing.html - blog w. code for VAE
http://arxiv.org/pdf/1312.6114v10.pdf - Variational Auto Encoder
https://jmetzen.github.io/2015-11-27/vae.html - tf code for variational auto-encoder
https://www.youtube.com/watch?v=P78QYjWh5sM

https://arxiv.org/pdf/1401.4082.pdf - stochastic backpropagation and approx inference - deep mind
http://www.cs.toronto.edu/~fritz/absps/colt93.html - keep neural simple by minimizing descr length - hinton
https://github.com/vivanov879/draw - code

June 20, 2016 - Penninsula

Recurrent models of visual attention - Deep Mind
https://papers.nips.cc/paper/5542-recurrent-models-of-visual-attention.pdf

June 23, 29 2016 - SF

http://arxiv.org/pdf/1410.5401v2.pdf - Neural Turing Machines - Graves et. al.
https://arxiv.org/pdf/1605.06065v1.pdf - One Shot Learning - DeepMind
http://www.shortscience.org/paper?bibtexKey=journals/corr/1605.06065 - Larochell comments on One-Shot paper
https://github.com/shawntan/neural-turing-machines - Code
https://www.reddit.com/r/MachineLearning/comments/2xcyrl/i_am_j%C3%BCrgen_schmidhuber_ama/cp4ecce - schmidhuber's comments
http://www.thespermwhale.com/jaseweston/ram/papers/paper_16.pdf
http://snowedin.net/tmp/Hochreiter2001.pdf - Reviews:
http://icml.cc/2016/reviews/839.txt
Code https://github.com/brendenlake/omniglot https://github.com/tristandeleu/ntm-one-shot https://github.com/MLWave/extremely-simple-one-shot-learning

June 13, 2016 - TBD, Penninsula

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning:
http://arxiv.org/pdf/1602.07261v1.pdf

June 9, 2016 - Galvanize

Visualizing and Understanding RNN:
https://arxiv.org/pdf/1506.02078v2.pdf

June 6, 2016 - Hacker Dojo

Google inception paper - origin of 1x1 convolution layers
http://arxiv.org/pdf/1409.4842v1.pdf

June 2, May 26, 2016 - Galvanize

Image segmentation with deep encoder-decoder

https://arxiv.org/pdf/1511.00561.pdf

May 23, 2016 - Hacker Dojo

Compressed networks, reducing flops by pruning

https://arxiv.org/pdf/1510.00149.pdf

http://arxiv.org/pdf/1602.07360v3.pdf

May 16, 2016

Word2Vec meets LDA:

http://arxiv.org/pdf/1605.02019v1.pdf - Paper

https://twitter.com/chrisemoody - Chris Moody's twiter with links to slides etc.

http://qpleple.com/topic-coherence-to-evaluate-topic-models/ - writeup on topic coherence

May 9, 2016

https://arxiv.org/pdf/1603.05027v2.pdf - Update on microsoft resnet - identity mapping

http://gitxiv.com/posts/MwSDm6A4wPG7TcuPZ/recurrent-batch-normalization - batch normalization w. RNN

May 2, 2016

Go playing DQN - AlphaGo

https://gogameguru.com/i/2016/03/deepmind-mastering-go.pdf

https://m.youtube.com/watch?sns=em&v=pgX4JSv4J70 - video of slide presentation on paper

https://en.m.wikipedia.org/wiki/List_of_Go_games#Lee.27s_Broken_Ladder_Game - Handling "ladders" in alphgo

https://en.m.wikipedia.org/wiki/Ladder_(Go) - ladders in go


April 25, 2016 - Microsoft Resnet

The Paper

http://arxiv.org/pdf/1512.03385v1.pdf

References:

http://arxiv.org/pdf/1603.05027v2.pdf - Identity mapping paper

Code:

https://keunwoochoi.wordpress.com/2016/03/09/residual-networks-implementation-on-keras/ - keras code

https://github.com/ry/tensorflow-resnet/blob/master/resnet.py - tensorflow code

https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/skflow/resnet.py


April 18, 2016 - Batch Normalization

The Paper
https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf
http://gitxiv.com/posts/MwSDm6A4wPG7TcuPZ/recurrent-batch-normalization - Batch Normalization for RNN


April 11, 2016 - Atari Game Playing DQN

The Paper https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf)

Related references:

This adds 'soft' and 'hard' attention and the 4 frames are replaced with an LSTM layer:

http://gitxiv.com/posts/NDepNSCBJtngkbAW6/deep-attention-recurrent-q-network

http://home.uchicago.edu/~arij/journalclub/papers/2015_Mnih_et_al.pdf - Nature Paper

http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html - videos at the bottom of the page

http://llcao.net/cu-deeplearning15/presentation/DeepMindNature-preso-w-David-Silver-RL.pdf - David Silver's slides

http://www.cogsci.ucsd.edu/~ajyu/Teaching/Cogs118A_wi09/Class0226/dayan_watkins.pdf

http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html - David Silver

Implementation Examples:

http://stackoverflow.com/questions/35394446/why-doesnt-my-deep-q-network-master-a-simple-gridworld-tensorflow-how-to-ev?rq=1

http://www.danielslater.net/2016/03/deep-q-learning-pong-with-tensorflow.html


March 3, 2016 Gated Feedback RNN

The Paper

"Gated RNN" (http://arxiv.org/pdf/1502.02367v4.pdf

-Background Material

http://arxiv.org/pdf/1506.00019v4.pdf - Lipton's excellent review of RNN
http://www.nehalemlabs.net/prototype/blog/2013/10/10/implementing-a-recurrent-neural-network-in-python/ - Discussion of RNN and theano code for Elman network - Tiago Ramalho
http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf - Hochreiter's original paper on LSTM
https://www.youtube.com/watch?v=izGl1YSH_JA - Hinton video on LSTM

-Skylar Payne's GF RNN code
https://github.com/skylarbpayne/hdDeepLearningStudy/tree/master/tensorflow

-Slides https://docs.google.com/presentation/d/1d2keyJxRlDcD1LTl_zjS3i45xDIh2-QvPWU3Te29TuM/edit?usp=sharing
https://github.com/eadsjr/GFRNNs-nest/tree/master/diagrams/diagrams_formula

Reviews

http://www.computervisionblog.com/2016/06/deep-learning-trends-iclr-2016.html
https://indico.io/blog/iclr-2016-takeaways/



hdDeepLearningStudy

深入学习小组的论文,代码等 #未来阅读的建议 https://arxiv.org/pdf/1605.06431v1.pdf - 深层网络合集 https://arxiv.org/pdf/1602.08124v3.pdf - 并行化的方法 https://arxiv.org/pdf/1404.5997v2.pdf - 并行计算问题 http://www.wsdm-conference.org/2016/slides/WSDM2016-Jeff- Dean.pdf - 分布式架构 https://www.youtube.com/watch?v=sUzQpd-Ku4o - jeff dean的视频说话 https://arxiv.org/pdf/1611.01578v1.pdf - 用于寻找神经架构的RL http://mlg.eng.cam.ac.uk/yarin/blog_2248.html - 神经网络的不确定性 https://arxiv.org/pdf/1611.01587.pdf - 联合多任务模型:多个NLP任务的神经网络 - 苏格 http://论文。 nips.cc/paper/5773-deep-generative-image-models-using-a-laplacian-pyramid-of-adversarial-networks.pdf -GAN论文(LeCun的recc)

https://arxiv.org/pdf/1511.05440.pdf - 视频预测GAN https://arxiv.org/abs/1703.02528 - 生成的非对抗网络 https://arxiv.org/pdf/1611.01578.pdf - 使用RL的神经体系结构搜索 - 谷歌脑 https://arxiv.org/pdf/1703.01041.pdf - 图像分类器的大规模演进 - google brain < p>

4月10日 - 黑客Dojo

https://arxiv.org/pdf/1603.08678.pdf - 实例敏感的完全卷积网络

https://arxiv.org/pdf/1611.07709.pdf - 完全卷积实例感知语义分割

4月3日 - 黑客Dojo

https://arxiv.org/pdf/1703.03864.pdf - 关于使用进化系统优化RL的Sutskever论文问题 http://jmlr.csail.mit.edu/papers/volume15/wierstra14a/wierstra14a.pdf < / a> - 在Sutskever纸上使用算法的ES纸

https://arxiv.org/pdf/1612.05424.pdf - 无监督像素级域名适应与生成对抗网络

https://arxiv.org/pdf/1701.06547.pdf - 神经对话生成的对抗学习

2月27日 - 黑客Dojo

https://arxiv.org/pdf/1612.02699.pdf - 深度监督与形状概念的遮挡意识解析3D对象 Zeeshan的幻灯片放在他的名字的文件夹中。除了他对自己开创性作品的描述外,他也提供了从2d图像中识别3d物体的卓越历史。

2月20日 - 黑客Dojo

https://arxiv.org/pdf/1506.07285.pdf - 问我什么 - Socher https://github.com/YerevaNN/Dynamic-memory-networks-in-Theano - 代码和实施说明 https://www.youtube.com/watch?v=FCtpHt6JEI8&t=27s - Socher介绍材料

2月13日 - 黑客Dojo

https://arxiv.org/pdf/1701.06538v1.pdf - 非常大的神经网络

2月6日 - 黑客Dojo

https://arxiv.org/pdf/1505.00387v2.pdf - 公路网络 https://arxiv.org/pdf/1507.06228.pdf - 也是公路网 - 不同的例子 https://arxiv.org/pdf/1607.03474v3.pdf - 经常性公路网络

1月30日 - 黑客Dojo

https://arxiv.org/pdf/1603.03116v2.pdf - 低级传球RNN的后续的单一的rnn https://github.com/Avmb/lowrank-gru - theano代码

1月23日 - HackerDojo

https://arxiv.org/abs/1612.03242 - Stack Gan Paper https://github.com/hanzhanggit/StackGAN - 代码

1月16日 - 黑客Dojo

https://arxiv.org/pdf/1511.06464v4.pdf - 单一进化RNN https://github.com/amarshah/complex_RNN - theano代码

1月9日 - 黑客Dojo

Cheuksan爱德华王话 https://arxiv.org/pdf/1612.04642v1.pdf - 旋转不变量cnn
https://github.com/deworrall92/harmonicConvolutions - 用于谐波的tf代码 http://visual.cs.ucl.ac.uk/pubs/harmonicNets/index.html < / a> - 作者的博客文章

1月2日 - 黑客Dojo

https://arxiv.org/pdf/1602.02218v2.pdf - 使用打字来改善RNN行为< br /> http://jmlr.org/proceedings/papers/v37/jozefowicz15.pdf - 探索替代LSTM架构

12月19日 - 黑客Dojo

https://arxiv.org/pdf/1611.01576.pdf - Socher qRnn论文

12月12日 - 黑客Dojo

https://arxiv.org/pdf/1604.02135v2.pdf - 最新细分公平 https://github.com/MarvinTeichmann/tensorflow-fcn - 分段代码

12月5日 - 黑客Dojo

https://arxiv.org/pdf/1506.06204.pdf - 对象分割 https://arxiv.org/pdf/1603.08695v2.pdf - 细化上述分段纸张 https://code.facebook.com/posts/561187904071636/segmenting-and -refining-images-with-sharpmask / - 博客帖子 https://github.com/facebookresearch/deepmask - 深层掩码的torch代码

11月28日 - 黑客Dojo

https://arxiv.org/pdf/1506.01497v3.pdf
people.eecs.berkeley.edu/~rbg/slides/rbg-defense-slides.pdf - Girshick论文幻灯片 检查边框和选择性搜索 https://arxiv.org/pdf/1406.4729v4.pdf - 架构的关键部分 https://github.com/smallcorgi/Faster-RCNN_TF - 出色的代码

11月21日 - 黑客Dojo

https://people.eecs.berkeley.edu/~rbg/papers /r-cnn-cvpr.pdf - RCNN https://arxiv.org/pdf/1504.08083v2.pdf - RCNN - 系列首先 https://arxiv.org/pdf/1506.01497v3.pdf - 更快的R-CNN http://techtalks.tv/talks/rich Girshick Talk的视频的特征级别 - 准确 - 对象检测和语义分割/ 60254 / 视频

11月14日 - 黑客道场

https://arxiv.org/pdf/1506.02025v3.pdf - 空间变压器网络 https://github.com/daviddao/spatial-transformer-tensorflow - 上述的tf代码

10月31日 - 黑客Dojo

https://github.com/jazzsaxmafia/show_attend_and_tell.tensorflow - 关注字幕的代码 http://cs.stanford.edu/people/karpathy/densecap/ - karpathy captioning https://arxiv.org/pdf/1412.2306v2.pdf - 较早的karpathy标题文件

10月20日 - 镀锌

https://webdocs.cs.ualberta.ca/~sutton/book/the -book.html - 深入了解强化学习 - 萨顿和巴托 - 第1章和第2章

10月17日 - 黑客Dojo

https://arxiv.org/pdf/1608.06993v1.pdf - DenseNet。新统治冠军形象分类器 https://github.com/liuzhuang13/DenseNet - lua代码 DenseNet纸是直接的,所以我们也将从图像字幕开始

http://www.cs.toronto.edu/~zemel/documents/captionAttn.pdf
http://kelvinxu.github.io/projects/capgen.html
http://people.ee.duke.edu/~lcarin/Yunchen9.25.2015.pdf - 标题注意的幻灯片

字幕集合。 https://github.com/kjw0612/awesome-deep-vision#image-captioning - 图片 https://github.com/kjw0612/awesome-deep-vision#video-captioning - 视频

10月13日 - SF

http://www.mit.edu/~dimitrib/NDP_Encycl.pdf - (早) Bersekas关于RL的论文,政策和价值的重复 http://www.nervanasys.com / demystifying-deep-reinforcement-learning /?imm_mid = 0e2d7e&amp; cmp = em-data-na-na-newsltr_20160420 - RL上的博文。价值迭代的良好覆盖

10月10日 - 黑客Dojo

https://github.com/carpedm20/pixel-rnn-tensorflow - 像素rnn的张量流代码(和cnn)

9月19日 - 黑客Dojo

https://arxiv.org/pdf/1606.05328v2.pdf - 使用PixelCNN解码器的条件图像生成< br /> https://arxiv.org/pdf/1601.06759v3.pdf - 像素RNN
https://drive.google.com/file/d/0B3cxcnOkPx9AeWpLVXhkTDJINDQ/view - wavenet生成音频< br /> https://deepmind.com/blog/wavenet-generative-model-raw-audio/ - wavenet博客

9月15日 - 镀锌SF

http://www.gitxiv.com/posts/fepYG4STYaej3KSPZ/densely-connected -convolutional-netowork-densenet

9月12日 - 黑客Dojo

http://arxiv.org/pdf/1410.3916v11.pdf - 原始内存网络 https://arxiv.org/pdf/1606.03126v1.pdf - 键/值内存增加nn http://www.thespermwhale.com/jaseweston/icml2016/icml2016-memnn- tutorial.pdf#page = 87 - 有关语言理解的内存网络教程

8月29日 - 黑客Dojo

https://arxiv.org/pdf/1410.5401v2.pdf - 神经图灵机器 https://github.com/carpedm20/NTM-tensorflow
https://www.youtube.com/watch?v=_H0i0IhEO2g - Alex Graves微软研究演讲/> http://www.robots.ox.ac.uk/~tvg/publications/谈话/ NeuralTuringMachines.pdf - 幻灯片为ntm

8月25日 - 镀锌(SF)

http://arxiv.org/pdf/1410.3916v11.pdf - 原始内存网络 https://arxiv.org/pdf/1606.03126v1.pdf - 键/值内存增加nn http://www.thespermwhale.com/jaseweston/icml2016/icml2016-memnn- tutorial.pdf#page = 87 - 有关语言理解的内存网络教程

8月22日 - 黑客Dojo

https://arxiv.org/pdf/1605.07648v1.pdf - 分形网 - 替代resnet超深卷积 https://github.com/edgelord/FractalNet - tf code http://www.gitxiv.com/posts/ibA8QEu8bvBJSDxr9/fractalnet-超深层神经网络 - 无残差

2016年8月18日 - 镀锌(SF)

https://arxiv.org/pdf/1602.01783v2.pdf - 新的RL架构 - 深思熟虑< / p>

代码: https://github.com/Zeta36/Asynchronous-Methods-for-Deep-Reinforcement-Learning < / a> - tf https://github.com/miyosuda/async_deep_reinforce - tf https://github.com/coreylynch/async-rl - keras(tf)
https://github.com/muupan/async-rl - chainer(很好的讨论)

2016年8月15日 - Hacker Dojo

https://arxiv.org/pdf/1607.02533v1.pdf - 加强深入网络以达成对抗性示例。

2016年8月11日 - 镀锌(SF)

http://www.gitxiv.com/posts/HQJ3F9YzsQZ3eJjpZ/model-free-episodic - 控制 - 深刻的gitxiv纸和github上的代码 https://github.com/sudeepraja/Model-Free-Episodic-Control - 其他代码 https://github.com/ShibiHe/Model-Free-Episodic-Control

2016年8月8日 - Hacker Dojo

https://arxiv.org/pdf/1406.2661.pdf - 关于生成对抗网(gan)的原始论文 - goodfellow,bengio http://arxiv.org/pdf/1511.06434v2.pdf - deep cnn gan - radford https://github.com/Newmu/dcgan_code - cnn gan - radford的theano代码

2016年8月4日 - 镀锌(SF)

http://www.gitxiv.com/posts/HQJ3F9YzsQZ3eJjpZ/model-free-episodic -control - 深入思考gitxiv论文和github上的代码

2016年8月1日 - Hacker Dojo

论文 -
https://drive.google.com/file/d/0B8Dg3PBX90KNWG5KQXNQOFlBLU1JWWVONKN1UFpnbUR6Y0cw/view? pref = 2&amp; pli = 1 - 使用随机RNN进行时间异常检测 https://home.zhaw.ch/~dueo/bbs/files/vae.pdf https://arxiv.org/pdf/1401.4082v3.pdf - Rezende - 其他原创VAE论文

代码审查 -
https://github.com/oduerr/dl_tutorial/blob/master/tensorflow/vae/ vae_demo.ipynb
https://github.com/oduerr/dl_tutorial/blob/master/tensorflow/ vae / vae_demo-2D.ipynb

2016年7月28日 - SF

论文:
http://arxiv.org/pdf/1410.5401v2.pdf - 神经图灵机 - Graves et。等 https://arxiv.org/pdf/1605.06065v1.pdf - 单次学习 - DeepMind

代码:
http://icml.cc/2016/reviews/839.txt
https://github.com/brendenlake/omniglot
https://github.com/tristandeleu/ntm-one-shot
https://github.com/MLWave/extremely-simple-one-shot-learning < / p>

2016年7月25日 - Hacker Dojo

论文 - 使用VAE进行异常检测 https://arxiv.org/pdf/1411.7610.pdf - 随机复制网络 https://drive.google.com/file/d/0B8Dg3PBX90KNWG5KQXNQOFlBLU1JWWVONKN1UFpnbUR6Y0cw/view? pref = 2&amp; pli = 1 - 使用随机RNN进行时间异常检测

2016年7月21日 - SF

阅读论文:
http://www.thespermwhale.com/jaseweston/ram/papers/paper_16.pdf < br /> http://snowedin.net/tmp/Hochreiter2001.pdf -

评论/代码 http://icml.cc/2016/reviews/839.txt
https://github.com/brendenlake/omniglot
https://github.com/tristandeleu/ntm-one-shot
https://github.com/MLWave/extremely-simple-one-shot-learning < br /> https://www.periscope.tv/hugo_larochelle/1ypJdnPRYEoKW

2016年7月18日 - 黑客Dojo

阅读论文:
http://arxiv.org/pdf/1312.6114v10.pdf - 变体自动编码器 - 阿姆斯特丹 - 金马与威灵
http://arxiv.org/pdf/1310.8499v2.pdf - 深刻的自回归网络 - 深思熟虑 https://arxiv.org/abs/1606.05908 - vae教程

评论/代码 https://jmetzen.github.io/2015-11-27/vae.html - metzen - 代码和讨论 http://blog.keras.io/building-autoencoders-in-keras.html - chollet - 讨论不同的自动编码器,给出keras代码。

2016年6月27日,7月11日 - Hacker Dojo

图像生成的经常性网络 - 深层次 https://arxiv.org/pdf/1502.04623v2.pdf
背景和一些参考文献 http://blog.evjang.com/2016/06/understanding-and-implementing.html < / a> - 博客w。 VAE的代码 http://arxiv.org/pdf/1312.6114v10.pdf - 变体自动编码器 https://jmetzen.github.io/2015-11-27/vae.html - 变分自动编码器的tf代码 https://www.youtube.com/watch?v=P78QYjWh5sM

https://arxiv.org/pdf/1401.4082.pdf - 随机反向传播和大概推论 - 深思熟虑< br /> http://www.cs.toronto.edu/~fritz/absps/colt93.html - 通过最小化descr长度来保持神经简单 - hinton https://github.com/vivanov879/draw - 代码

2016年6月20日 - Penninsula

视觉注意的反复模式 - 深层次 https://papers.nips.cc/paper/5542-recurrent-models- of-visual-attention.pdf

2016年6月23日,2016 - SF

http://arxiv.org/pdf/1410.5401v2.pdf - 神经图灵机 - Graves et。等 https://arxiv.org/pdf/1605.06065v1.pdf - 单击学习 - DeepMind
http://www.shortscience.org/paper?bibtexKey=journals/corr/1605.06065 - 拉罗切尔对单张纸的评论 https://github.com/shawntan/neural-turing-machines - 代码 https://www.reddit.com/r/MachineLearning/comments/2xcyrl/ i_am_j%C3%BCrgen_schmidhuber_ama / cp4ecce - schmidhuber的评论 http://www.thespermwhale.com/jaseweston/ram/papers/paper_16.pdf < br /> http://snowedin.net/tmp/Hochreiter2001.pdf - 评论:
http://icml.cc/2016/reviews/839.txt
https://github.com/brendenlake/omniglot https://github.com/tristandeleu/ntm-one-shot https://github.com/MLWave/extremely-simple-one-shot-learning < / p>

2016年6月13日 - TBD,Penninsula

开始-v4,Inception-ResNet和剩余连接对学习的影响:
http://arxiv.org/pdf/1602.07261v1.pdf

2016年6月9日 - 镀锌

可视化和了解RNN:
https://arxiv.org/pdf/1506.02078v2.pdf

2016年6月6日 - Hacker Dojo

Google初始论文 - 1x1卷积层的起源 http://arxiv.org/pdf/1409.4842v1.pdf

2016年6月2日,2016年5月26日 - 镀锌

使用深度编码器解码器的图像分割

https://arxiv.org/pdf/1511.00561.pdf

2016年5月23日 - Hacker Dojo

压缩网络,通过修剪减少翻牌

https://arxiv.org/pdf/1510.00149.pdf

http://arxiv.org/pdf/1602.07360v3.pdf

五月16, 2016

Word2Vec符合LDA:

http://arxiv.org/pdf/1605.02019v1.pdf - 论文

https://twitter.com/chrisemoody - Chris Moody的抽签机,其中包含幻灯片等。

http://qpleple.com/topic-coherence-to-evaluate-topic-models/ - 主题一致性的写入

May 9, 2016

https://arxiv.org/pdf/1603.05027v2.pdf - 微软resnet更新 - 身份映射

http://gitxiv.com/posts/MwSDm6A4wPG7TcuPZ/recurrent-batch-normalization - 批次正常化RNN

May 2, 2016

播放DQN - AlphaGo

https://gogameguru.com/i/2016/03/deepmind-mastering-go .pdf

https://m.youtube.com/watch?sns=em&v=pgX4JSv4J70 - 纸上幻灯片演示的视频

https://en.m.wikipedia.org/wiki/List_of_Go_games#Lee.27s_Broken_Ladder_Game - 在alphgo中处理梯子

https://en.m.wikipedia.org/wiki/Ladder_(Go) - 梯子走了


2016年4月25日 - Microsoft Resnet

论文

http://arxiv.org/pdf/1512.03385v1.pdf

参考文献:

http://arxiv.org/pdf/1603.05027v2.pdf - 身份映射文件

代码:

https://keunwoochoi.wordpress.com/2016/03/ 09 /残留网络实施在keras / - keras代码

https://github.com/ry/tensorflow-resnet/blob/master/resnet .py - 张量流代码

https://github.com/tensorflow/tensorflow/blob/master /tensorflow/examples/skflow/resnet.py


2016年4月18日 - 批量规范化

纸张 https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf
http://gitxiv.com/posts/MwSDm6A4wPG7TcuPZ/recurrent-batch-normalization - RNN的批次归一化


2016年4月11日 - Atari游戏播放DQN

论文 https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf

相关参考文献:

这增加了软和硬的注意力,4个框架被替换为LSTM层:

http://gitxiv.com/posts/NDepNSCBJtngkbAW6/deep-attention-recurrent-q -network

http://home.uchicago.edu/~arij/journalclub/papers/2015_Mnih_et_al.pdf - 自然纸

http://www.nature.com/nature/journal/v518/n7540 /full/nature14236.html - 页面底部的视频

http://llcao.net/cu-deeplearning15/presentation /DeepMindNature-preso-w-David-Silver-RL.pdf - David Silver的幻灯片

http://www.cogsci.ucsd.edu/~ajyu/教学/Cogs118A_wi09/Class0226/dayan_watkins.pdf

http://www0.cs.ucl.ac.uk/staff /d.silver/web/Teaching.html - David Silver

实现示例:

http://stackoverflow.com/questions/35394446/why-doesnt-my-deep-q-network-master-a-simple-gridworld-tensorflow-how-to-ev?rq=1

http://www.danielslater.net/2016/03 /deep-q-learning-pong-with-tensorflow.html


2016年3月3日封闭反馈RNN

论文

Gated RNN( http://arxiv.org/pdf/1502.02367v4.pdf

- 背景材料

http://arxiv.org/pdf/1506.00019v4.pdf - Lipton对RNN的优秀评论
http://www.nehalemlabs.net / prototype / blog / 2013/10/10 / implementation-a-recurrent-neural-network-in-python / - 关于Elman网络的RNN和theano代码的讨论 - Tiago Ramalho号 http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf - Hochreiter的原始论文LSTM
https://www.youtube.com/watch?v=izGl1YSH_JA - 关于LSTM的Hinton视频

-Sanear Payne的GF RNN码 https://github.com/skylarbpayne/hdDeepLearningStudy/tree/master/tensorflow

-Slides https://docs.google.com/presentation/d/1d2keyJxRlDcD1LTl_zjS3i45xDIh2-QvPWU3Te29TuM/edit? usp =分享 https://github.com/eadsjr/GFRNNs-nest/tree/master/diagrams/diagrams_formula < / a>

评论

http://www.computervisionblog.com/2016/06/deep -learning-trends-iclr-2016.html https://indico.io/blog/iclr-2016-takeaways/




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