Code etc for Deep Learning Study Group
Recurrent network for image generation - Deep Mind
https://arxiv.org/pdf/1502.04623.pdf
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
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
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/