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chainer-info's Introduction

Chainer Info

What is Chainer?

Chainer is a flexible framework for neural networks. One major goal is flexibility, so it must enable us to write complex architectures simply and intuitively.

More info here

ref Chainer.wiki - Chainer Wiki

Table of Contents

## Tutorials * [Chainer Tutorial Offical document]http://docs.chainer.org/en/latest/tutorial/index.html) - Chainer Tutorial Offical document

Hands-on

## Models/Projects ### Preferred Networks official * [ChainerRL](https://github.com/pfnet/chainerrl) - ChainerRL is a deep reinforcement learning library built on top of Chainer. * [Paint Chainer](https://github.com/pfnet/PaintsChainer) - Paints Chainer is line drawing colorizer using chainer. ## Examples * [mitmul/chainer-cifar10](https://github.com/mitmul/chainer-cifar10) - Cifar10 * [mitmul/DeepPose](https://github.com/mitmul/deeppose) - Deep pose * [ugo-nama-kun/DQN-chainer](https://github.com/ugo-nama-kun/DQN-chainer) - Deep Q-Network (DQN) * [mrkn/chainer-srcnn](https://github.com/mrkn/chainer-srcnn) - Image super-resolution * [Hi-king/chainer_superresolution](https://github.com/Hi-king/chainer_superresolution) - Image super-resolution * [darashi/chainer-example-overfeat-classify](https://github.com/darashi/chainer-example-overfeat-classify) - Overfeat * [yusuketomoto/chainer-char-rnn](https://github.com/yusuketomoto/chainer-char-rnn) - Recurrent neural network (RNN) * [RyotaKatoh/chainer-Variational-AutoEncoder](https://github.com/RyotaKatoh/chainer-Variational-AutoEncoder) - Variational autoencoder (VAE) * [mitmul/chainer-imagenet-vgg](https://github.com/mitmul/chainer-imagenet-vgg) - VGG word segmentation - Machine Translation * [yasunorikudo/chainer-ResNet](https://github.com/yasunorikudo/chainer-ResNet) - ResNet * [yasunorikudo/chainer-DenseNet](https://github.com/yasunorikudo/chainer-DenseNet) - DenseNet * [yasunorikudo/chainer-ResDrop](https://github.com/yasunorikudo/chainer-ResDrop) - ResDrop * [mitmul/chainer-conv-vis](https://github.com/mitmul/chainer-conv-vis) - Convolution Filter Visualization * [mitmul/chainer-siamese](https://github.com/mitmul/chainer-siamese) - Siamese Network * [mitmul/chainer-svm](https://github.com/mitmul/chainer-svm) - Support Vector Machine (SVM) * [mitmul/chainer-fast-rcnn](https://github.com/mitmul/chainer-fast-rcnn) - Fast R-CNN * [apple2373/chainer-simple-fast-rnn](https://github.com/apple2373/chainer-simple-fast-rnn) - Fast R-CNN * [mitmul/chainer-faster-rcnn](https://github.com/mitmul/chainer-faster-rcnn) - Faster R-CNN * [tscohen/GrouPy](https://github.com/tscohen/GrouPy) - Group Equivariant Convolutional Neural Networks * [yusuketomoto/chainer-fast-neuralstyle](https://github.com/yusuketomoto/chainer-fast-neuralstyle) - Perceptual Losses for Real-Time Style Transfer and Super-Resolution * [rezoo/illustration2vec](https://github.com/rezoo/illustration2vec) - illustration2vec * [apple2373/chainer_stylenet](https://github.com/apple2373/chainer_stylenet) - StyleNet (A Neural Algorithm of Artistic Style) * [mattya/chainer-gogh](https://github.com/mattya/chainer-gogh) - StyleNet (A Neural Algorithm of Artistic Style) * [apple2373/chainer_caption_generation](https://github.com/apple2373/chainer_caption_generation) - Show and Tell * [chainer-prednet](https://github.com/kunimasa-kawasaki/chainer-prednet) - Deep Predictive Coding Networks * [hillbig/binary_net](https://github.com/hillbig/binary_net) - BinaryNet * Generative Adversarial Nets (GAN) - Variational Auto-Encoder (VAE) * [rezoo/data.py](https://gist.github.com/rezoo/4e005611aaa4dad26697) - Generative Adversarial Nets (GAN) * [mattya/chainer-DCGAN](https://github.com/mattya/chainer-DCGAN) - Deep Convolutional Generative Adversarial Network (DCGAN) * [mattya/chainer-fluid](https://github.com/mattya/chainer-fluid) - Fluid simulation * [ktnyt/chainer_ca.py](https://gist.github.com/ktnyt/58e015dd9ff33049da5a) - Convolutional Autoencoder * [odashi/chainer_rnnlm.py](https://gist.github.com/odashi/0d6e259abcc14f2d2d28) - RNN Language Model * [odashi/chainer_encoder_decoder.py](https://gist.github.com/odashi/8d21f8fc23c075cd3042) - Neural Encoder-Decoder Machine Translation * [prajdabre/chainer_examples](https://github.com/prajdabre/chainer_examples/blob/master/chainer-1.5/LSTMVariants.py) - LSTM variants * [tochikuji/chainer-libDNN](https://github.com/tochikuji/chainer-libDNN/blob/master/examples/mnist/SdA.py) - Stacked Denoising Autoencoder * [masaki-y/ram](https://github.com/masaki-y/ram) - Recurrent Attention Model * [wkentaro/fcn](https://github.com/wkentaro/fcn) - Fully Convolutional Networks * [hvy/chainer-gan-denoising-feature-matching](https://github.com/hvy/chainer-gan-denoising-feature-matching) - Generative Adversarial Networks with Denoising Feature Matching * [hvy/chainer-visualization](https://github.com/hvy/chainer-visualization) - Visualizing and Understanding Convolutional Networks * [hvy/chainer-gan-trainer](https://github.com/hvy/chainer-gan-trainer) - Chainer GAN Trainer * [jekbradbury/qrnn.py](http://metamind.io/research/new-neural-network-building-block-allows-faster-and-more-accurate-text-understanding/) - QRNN * [dsanno/chainer-dfi](https://github.com/dsanno/chainer-dfi) - Deep Feature Interpolation for Image Content Changes * [mitmul/chainer-segnet](https://github.com/mitmul/chainer-segnet) - SegNet ## Libraries ##Videos ##Papers * [Temporal Generative Adversarial Nets](https://arxiv.org/abs/1611.06624) - arXiv only * [Reasoning with Memory Augmented Neural Networks for Language Comprehension](https://arxiv.org/abs/1610.06454) - arXiv only * [PMI Matrix Approximations with Applications to Neural Language Modeling](https://arxiv.org/abs/1609.01235) - arXiv only * [Neural Tree Indexers for Text Understanding](https://arxiv.org/abs/1607.04492) - arXiv only * [Neural Semantic Encoders](https://arxiv.org/abs/1607.04315) - arXiv only * [Networked Intelligence: Towards Autonomous Cyber Physical Systems](https://arxiv.org/abs/1606.04087) - arXiv only * [Modeling the dynamics of human brain activity with recurrent neural networks](https://arxiv.org/abs/1606.03071) - arXiv only * [A Deep-Learning Approach for Operation of an Automated Realtime Flare Forecast](https://arxiv.org/abs/1606.01587) - arXiv only * [Convolutional Neural Networks using Logarithmic Data Representation](https://arxiv.org/abs/1603.01025) - arXiv only * [context2vec: Learning Generic Context Embedding with Bidirectional LSTM](http://u.cs.biu.ac.il/%7Emelamuo/publications/context2vec_conll16.pdf) - CoNLL 2016 * [Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec](https://arxiv.org/abs/1605.02019) - CoNLL 2016 * [Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition](https://arxiv.org/abs/1609.05119) - ECCV 2016 Workshop * [Learning Joint Representations of Videos and Sentences with Web Image Search](https://arxiv.org/abs/1608.02367) - ECCV 2016 Workshop * [Incorporating Discrete Translation Lexicons into Neural Machine Translation](https://arxiv.org/abs/1606.02006) - EMNLP 2016 * [Controlling Output Length in Neural Encoder-Decoders](https://arxiv.org/abs/1609.09552) - EMNLP 2016 * [Insertion Position Selection Model for Flexible Non-Terminals in Dependency Tree-to-Tree Machine Translation](http://www.aclweb.org/anthology/D16-1247) - EMNLP 2016 * [Learning Representations Using Complex-Valued Nets](https://arxiv.org/abs/1511.06351) - ICLR 2016 * [Dynamic Coattention Networks For Qustion Answering](https://arxiv.org/abs/1611.01604) - ICLR 2017 under review * [SqueezeNet: AlexNet-level Accuracy with 50x Fewer Parameters and < 0.5MB Model Size](https://arxiv.org/abs/1602.07360) - ICLR 2017 under review * [Quasi-Recurrent Neural Networks](https://arxiv.org/abs/1611.01576) - ICLR 2017 under review * [Steerable CNNs](https://arxiv.org/abs/1612.08498) - ICLR 2017 * [f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization](https://arxiv.org/abs/1606.00709) - NIPS 2016 Workshop * [QSGD: Randomized Quantization for Communication-Optimal Stochastic Gradient Descent](https://arxiv.org/abs/1610.02132) - OPT 2016 * [Evaluation of Deep Learning based Pose Estimation for Sign Language Recognition](https://arxiv.org/abs/1602.09065) - PETRA 2016 * [Machine-learning Selection of Optical Transients in Subaru/Hyper Suprime-Cam Survey](https://arxiv.org/abs/1609.03249) - PASJ 2016 * [A Deep-Learning Approach for Operation of an Automated Realtime Flare Forecast](https://arxiv.org/abs/1606.01587) - Space Weather 2016 * [Dynamic Entity Representation with Max-pooling Improves Machine Reading](http://aclweb.org/anthology/N/N16/N16-1099.pdf) - NAACL 2016 * [Feature-based Model versus Convolutional Neural Network for Stance Detection](http://aclweb.org/anthology/S/S16/S16-1065.pdf) - SemEval 2016 * [Cross-Lingual Image Caption Generation](https://www.aclweb.org/anthology/P/P16/P16-1168.pdf) - ACL 2016 * [Composing Distributed Representations of Relational Patterns](http://www.aclweb.org/anthology/P16-1215) - ACL 2016 * [Generating Natural Language Descriptions for Semantic Representations of Human Brain Activity](https://www.aclweb.org/anthology/P/P16/P16-3004.pdf) - ACL 2016 * [MetaMind Neural Machine Translation System for WMT 2016](https://aclweb.org/anthology/W/W16/W16-2308.pdf) - WMT 2016 * [Group Equivariant Convolutional Networks](https://arxiv.org/abs/1602.07576) - ICML 2016

Official announcements

Blog posts

## Community ### Global * [@ChainerOfficial on Twitter](https://twitter.com/ChainerOfficial) * [Mailing List](https://groups.google.com/forum/#!forum/chainer)

Japan

## Books * [Chainerによる実践深層学習](https://www.amazon.co.jp/dp/B01NBMKH21/ref=dp-kindle-redirect?_encoding=UTF8&btkr=1) by 新納浩幸

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