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
https://arxiv.org/pdf/1612.05424.pdf - Unsupervised Pixel-level domain adaptation with generative adversarial networks
https://arxiv.org/pdf/1612.02699.pdf - Deep Supervision with Shape Concepts for Occlusion-Aware 3D Object Parsing
https://arxiv.org/pdf/1701.06547.pdf - adversarial learning for neural dialog generation
https://arxiv.org/pdf/1506.07285.pdf - Ask me anything - Socher
https://arxiv.org/pdf/1701.06538v1.pdf - Outrageously large neural networks
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
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
https://arxiv.org/abs/1612.03242 - Stack Gan Paper
https://github.com/hanzhanggit/StackGAN - Code
https://arxiv.org/pdf/1511.06464v4.pdf - Unitary Evolution RNN https://github.com/amarshah/complex_RNN - theano code
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
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
https://arxiv.org/pdf/1611.01576.pdf - Socher qRnn paper
https://arxiv.org/pdf/1604.02135v2.pdf - latest segmentation fair
https://github.com/MarvinTeichmann/tensorflow-fcn - code for segmenter
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
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
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
https://arxiv.org/pdf/1506.02025v3.pdf - Spatial transformer networks
https://github.com/daviddao/spatial-transformer-tensorflow - tf code for above
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
https://webdocs.cs.ualberta.ca/~sutton/book/the-book.html - Deep dive into reinforcement learning - Sutton and Barto - Chapters 1 and 2.
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
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
https://github.com/carpedm20/pixel-rnn-tensorflow - tensorflow code for pixel rnn (and cnn)
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
http://www.gitxiv.com/posts/fepYG4STYaej3KSPZ/densely-connected-convolutional-netowork-densenet
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
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
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
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
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)
https://arxiv.org/pdf/1607.02533v1.pdf - Hardening deep networks to adversarial examples.
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
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
http://www.gitxiv.com/posts/HQJ3F9YzsQZ3eJjpZ/model-free-episodic-control - deep mind gitxiv paper and code on github
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
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
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
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
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.
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
Recurrent models of visual attention - Deep Mind
https://papers.nips.cc/paper/5542-recurrent-models-of-visual-attention.pdf
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
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning:
http://arxiv.org/pdf/1602.07261v1.pdf
Visualizing and Understanding RNN:
https://arxiv.org/pdf/1506.02078v2.pdf
Google inception paper - origin of 1x1 convolution layers
http://arxiv.org/pdf/1409.4842v1.pdf
Image segmentation with deep encoder-decoder
https://arxiv.org/pdf/1511.00561.pdf
Compressed networks, reducing flops by pruning
https://arxiv.org/pdf/1510.00149.pdf
http://arxiv.org/pdf/1602.07360v3.pdf
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
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
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
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
The Paper
https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf
http://gitxiv.com/posts/MwSDm6A4wPG7TcuPZ/recurrent-batch-normalization - Batch Normalization for RNN
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://www.danielslater.net/2016/03/deep-q-learning-pong-with-tensorflow.html
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
http://www.computervisionblog.com/2016/06/deep-learning-trends-iclr-2016.html
https://indico.io/blog/iclr-2016-takeaways/