deep learning paper implementation

TO-DO

- Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks http://arxiv.org/abs/1312.6082
- Multiple Object Recognition with Visual Attention https://arxiv.org/abs/1412.7755
- Recurrent Models of Visual Attention https://arxiv.org/abs/1406.6247
- DRAW: A Recurrent Neural Network For Image Generation https://arxiv.org/abs/1502.04623
- Neural Turing Machines https://arxiv.org/abs/1410.5401
- Pixel Recurrent Neural Networks https://arxiv.org/abs/1601.06759
- Conditional Image Generation with PixelCNN Decoders https://arxiv.org/abs/1606.05328
- Learning to learn by gradient descent by gradient descent https://arxiv.org/pdf/1606.04474v2.pdf
- Language Modeling with Gated Convolutional Networks https://arxiv.org/abs/1612.08083
- Show, Attend and Tell: Neural Image Caption Generation with Visual Attention https://arxiv.org/abs/1502.03044
- Maxout Networks https://arxiv.org/abs/1302.4389
- A Decomposable Attention Model for Natural Language Inference https://arxiv.org/pdf/1606.01933.pdf

Name		Name	Last commit message	Last commit date
Latest commit History 38 Commits
old		old
Draw-Gumbol-softmax-Stochastic-Generation.ipynb		Draw-Gumbol-softmax-Stochastic-Generation.ipynb
Draw-Gumbol-softmax.ipynb		Draw-Gumbol-softmax.ipynb
Draw-Stochsatic-Generation.ipynb		Draw-Stochsatic-Generation.ipynb
Draw.ipynb		Draw.ipynb
MultipleObjectRecognitionWithVisualAttention.ipynb		MultipleObjectRecognitionWithVisualAttention.ipynb
README.md		README.md
data_process.py		data_process.py
decomposable_attention.py		decomposable_attention.py
extract_feat.py		extract_feat.py
image_aug.py		image_aug.py
neural_turing_machine.ipynb		neural_turing_machine.ipynb
show_attend_tell_based_classification.py		show_attend_tell_based_classification.py
split_valid.py		split_valid.py
svhn.py		svhn.py
svhn_dataset.py		svhn_dataset.py
test.py		test.py