[
{
"path": "LICENSE",
"content": " Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n\n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n 1. Definitions.\n\n \"License\" shall mean the terms and conditions for use, reproduction,\n and distribution as defined by Sections 1 through 9 of this document.\n\n \"Licensor\" shall mean the copyright owner or entity authorized by\n the copyright owner that is granting the License.\n\n \"Legal Entity\" shall mean the union of the acting entity and all\n other entities that control, are controlled by, or are under common\n control with that entity. For the purposes of this definition,\n \"control\" means (i) the power, direct or indirect, to cause the\n direction or management of such entity, whether by contract or\n otherwise, or (ii) ownership of fifty percent (50%) or more of the\n outstanding shares, or (iii) beneficial ownership of such entity.\n\n \"You\" (or \"Your\") shall mean an individual or Legal Entity\n exercising permissions granted by this License.\n\n \"Source\" form shall mean the preferred form for making modifications,\n including but not limited to software source code, documentation\n source, and configuration files.\n\n \"Object\" form shall mean any form resulting from mechanical\n transformation or translation of a Source form, including but\n not limited to compiled object code, generated documentation,\n and conversions to other media types.\n\n \"Work\" shall mean the work of authorship, whether in Source or\n Object form, made available under the License, as indicated by a\n copyright notice that is included in or attached to the work\n (an example is provided in the Appendix below).\n\n \"Derivative Works\" shall mean any work, whether in Source or Object\n form, that is based on (or derived from) the Work and for which the\n editorial revisions, annotations, elaborations, or other modifications\n represent, as a whole, an original work of authorship. For the purposes\n of this License, Derivative Works shall not include works that remain\n separable from, or merely link (or bind by name) to the interfaces of,\n the Work and Derivative Works thereof.\n\n \"Contribution\" shall mean any work of authorship, including\n the original version of the Work and any modifications or additions\n to that Work or Derivative Works thereof, that is intentionally\n submitted to Licensor for inclusion in the Work by the copyright owner\n or by an individual or Legal Entity authorized to submit on behalf of\n the copyright owner. For the purposes of this definition, \"submitted\"\n means any form of electronic, verbal, or written communication sent\n to the Licensor or its representatives, including but not limited to\n communication on electronic mailing lists, source code control systems,\n and issue tracking systems that are managed by, or on behalf of, the\n Licensor for the purpose of discussing and improving the Work, but\n excluding communication that is conspicuously marked or otherwise\n designated in writing by the copyright owner as \"Not a Contribution.\"\n\n \"Contributor\" shall mean Licensor and any individual or Legal Entity\n on behalf of whom a Contribution has been received by Licensor and\n subsequently incorporated within the Work.\n\n 2. Grant of Copyright License. Subject to the terms and conditions of\n this License, each Contributor hereby grants to You a perpetual,\n worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n copyright license to reproduce, prepare Derivative Works of,\n publicly display, publicly perform, sublicense, and distribute the\n Work and such Derivative Works in Source or Object form.\n\n 3. Grant of Patent License. Subject to the terms and conditions of\n this License, each Contributor hereby grants to You a perpetual,\n worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n (except as stated in this section) patent license to make, have made,\n use, offer to sell, sell, import, and otherwise transfer the Work,\n where such license applies only to those patent claims licensable\n by such Contributor that are necessarily infringed by their\n Contribution(s) alone or by combination of their Contribution(s)\n with the Work to which such Contribution(s) was submitted. If You\n institute patent litigation against any entity (including a\n cross-claim or counterclaim in a lawsuit) alleging that the Work\n or a Contribution incorporated within the Work constitutes direct\n or contributory patent infringement, then any patent licenses\n granted to You under this License for that Work shall terminate\n as of the date such litigation is filed.\n\n 4. Redistribution. You may reproduce and distribute copies of the\n Work or Derivative Works thereof in any medium, with or without\n modifications, and in Source or Object form, provided that You\n meet the following conditions:\n\n (a) You must give any other recipients of the Work or\n Derivative Works a copy of this License; and\n\n (b) You must cause any modified files to carry prominent notices\n stating that You changed the files; and\n\n (c) You must retain, in the Source form of any Derivative Works\n that You distribute, all copyright, patent, trademark, and\n attribution notices from the Source form of the Work,\n excluding those notices that do not pertain to any part of\n the Derivative Works; and\n\n (d) If the Work includes a \"NOTICE\" text file as part of its\n distribution, then any Derivative Works that You distribute must\n include a readable copy of the attribution notices contained\n within such NOTICE file, excluding those notices that do not\n pertain to any part of the Derivative Works, in at least one\n of the following places: within a NOTICE text file distributed\n as part of the Derivative Works; within the Source form or\n documentation, if provided along with the Derivative Works; or,\n within a display generated by the Derivative Works, if and\n wherever such third-party notices normally appear. The contents\n of the NOTICE file are for informational purposes only and\n do not modify the License. You may add Your own attribution\n notices within Derivative Works that You distribute, alongside\n or as an addendum to the NOTICE text from the Work, provided\n that such additional attribution notices cannot be construed\n as modifying the License.\n\n You may add Your own copyright statement to Your modifications and\n may provide additional or different license terms and conditions\n for use, reproduction, or distribution of Your modifications, or\n for any such Derivative Works as a whole, provided Your use,\n reproduction, and distribution of the Work otherwise complies with\n the conditions stated in this License.\n\n 5. Submission of Contributions. Unless You explicitly state otherwise,\n any Contribution intentionally submitted for inclusion in the Work\n by You to the Licensor shall be under the terms and conditions of\n this License, without any additional terms or conditions.\n Notwithstanding the above, nothing herein shall supersede or modify\n the terms of any separate license agreement you may have executed\n with Licensor regarding such Contributions.\n\n 6. Trademarks. This License does not grant permission to use the trade\n names, trademarks, service marks, or product names of the Licensor,\n except as required for reasonable and customary use in describing the\n origin of the Work and reproducing the content of the NOTICE file.\n\n 7. Disclaimer of Warranty. Unless required by applicable law or\n agreed to in writing, Licensor provides the Work (and each\n Contributor provides its Contributions) on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n implied, including, without limitation, any warranties or conditions\n of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A\n PARTICULAR PURPOSE. You are solely responsible for determining the\n appropriateness of using or redistributing the Work and assume any\n risks associated with Your exercise of permissions under this License.\n\n 8. Limitation of Liability. In no event and under no legal theory,\n whether in tort (including negligence), contract, or otherwise,\n unless required by applicable law (such as deliberate and grossly\n negligent acts) or agreed to in writing, shall any Contributor be\n liable to You for damages, including any direct, indirect, special,\n incidental, or consequential damages of any character arising as a\n result of this License or out of the use or inability to use the\n Work (including but not limited to damages for loss of goodwill,\n work stoppage, computer failure or malfunction, or any and all\n other commercial damages or losses), even if such Contributor\n has been advised of the possibility of such damages.\n\n 9. Accepting Warranty or Additional Liability. While redistributing\n the Work or Derivative Works thereof, You may choose to offer,\n and charge a fee for, acceptance of support, warranty, indemnity,\n or other liability obligations and/or rights consistent with this\n License. However, in accepting such obligations, You may act only\n on Your own behalf and on Your sole responsibility, not on behalf\n of any other Contributor, and only if You agree to indemnify,\n defend, and hold each Contributor harmless for any liability\n incurred by, or claims asserted against, such Contributor by reason\n of your accepting any such warranty or additional liability.\n\n END OF TERMS AND CONDITIONS\n\n APPENDIX: How to apply the Apache License to your work.\n\n To apply the Apache License to your work, attach the following\n boilerplate notice, with the fields enclosed by brackets \"{}\"\n replaced with your own identifying information. (Don't include\n the brackets!) The text should be enclosed in the appropriate\n comment syntax for the file format. We also recommend that a\n file or class name and description of purpose be included on the\n same \"printed page\" as the copyright notice for easier\n identification within third-party archives.\n\n Copyright {yyyy} {name of copyright owner}\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n"
},
{
"path": "README.md",
"content": "# Awesome Chainer [](https://github.com/sindresorhus/awesome)\n\n## What is Chainer?\n\nChainer is a flexible framework for neural networks. One of the major goals is flexibility, so it must enable us to write complex architectures simply and intuitively.\n\nMore info [here](http://chainer.org/)\n\nref [Chainer.wiki](https://github.com/chainer/chainer/wiki) - Chainer Wiki\n\n## Table of Contents\n\n\n\n* [Tutorials](#github-tutorials)\n* [Models/Projects](#github-projects)\n* [Examples](#github-examples)\n* [Libraries](#libraries)\n* [Videos](#video)\n* [Papers](#papers)\n* [Blog posts](#blogs)\n* [Community](#community)\n* [Books](#books)\n\n\n\n\n\n## Guides\n\n* [Chainer Offical Guides](https://docs.chainer.org/en/stable/guides/index.html) - Chainer guides in the offical document\n\n## Hands-on\n\n*\t[hido/chainer-handson](https://github.com/hido/chainer-handson/blob/master/chainer.ipynb) - Chainer Hands-on\n*\t[iwiwi/chainer-handson](https://github.com/iwiwi/chainer-handson/blob/master/chainer-ja.ipynb) - Chainer Hands-on(JP CPU only)\n* [mitmul/chainer-handson](https://github.com/mitmul/chainer-handson) - Chainer Hands-on\n* [mitmul/chainer-notebooks](https://github.com/mitmul/chainer-notebooks) - Chainer Jupyter Notebooks\n\n\n\n## Models/Projects\n\n### Official Add-on Packages\n\n* [ChainerRL](https://github.com/chainer/chainerrl) - ChainerRL is a deep reinforcement learning library built on top of Chainer.\n* [ChainerCV](https://github.com/chainer/chainercv) - Versatile set of tools for Deep Learning based Computer Vision\n* [ChainerMN](https://github.com/chainer/chainermn) - Scalable distributed deep learning with Chainer\n* [ChainerUI](https://github.com/chainer/chainerui) - ChainerUI is a visualization and management tool for Chainer.\n\n\n### Services using Chainer\n\n* [PaintsChainer](https://github.com/pfnet/PaintsChainer) - Paints Chainer is line drawing colorizer using chainer.\n\n\n\n# Chainer External Examples\n\n* [chainer/models](https://github.com/chainer/models) - Models and examples built with Chainer\n\n## Preferred Networks Research\n\n* [chainer-LSGAN](https://github.com/pfnet-research/chainer-LSGAN) - Least Squares Generative Adversarial Network implemented in Chainer\n* [chainer-gogh](https://github.com/pfnet-research/chainer-gogh) - Implementation of \"A neural algorithm of Artistic style\"\n* [chainer-graph-cnn](https://github.com/pfnet-research/chainer-graph-cnn) - Chainer implementation of 'Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering' (https://arxiv.org/abs/1606.09375)\n* [chainer-segnet](https://github.com/pfnet-research/chainer-segnet) - SegNet implementation & experiments in Chainer\n* [chainer-pix2pix](https://github.com/pfnet-research/chainer-pix2pix) - chainer implementation of pix2pix\n* [chainer-ADDA](https://github.com/pfnet-research/chainer-ADDA) - Adversarial Discriminative Domain Adaptation\n* [chainer-gan-lib](https://github.com/pfnet-research/chainer-gan-lib) - Various GANs implementation\n* [tgan](https://github.com/pfnet-research/tgan) - Temporal Generative Adversarial Nets\n\n### NLP\n\n * [odashi/chainer_examples](https://github.com/odashi/chainer_examples) - Machine Translation, word segmentation, and language model\n * [odashi/chainer_rnnlm.py](https://gist.github.com/odashi/0d6e259abcc14f2d2d28) - RNN Language Model\n * [odashi/chainer_encoder_decoder.py](https://gist.github.com/odashi/8d21f8fc23c075cd3042) - Neural Encoder-Decoder Machine Translation\n * [prajdabre/chainer_examples](https://github.com/prajdabre/chainer_examples/blob/master/chainer-1.5/LSTMVariants.py) - LSTM variants\n * [yusuketomoto/chainer-char-rnn](https://github.com/yusuketomoto/chainer-char-rnn) - Recurrent neural network (RNN)\n * [mlpnlp/mlpnlp-nmt](https://github.com/mlpnlp/mlpnlp-nmt) - LSTM encoder-decoder with attention mechanism\n * [unnonouno/chainer-memnn](https://github.com/unnonouno/chainer-memnn) - End-to-end memory networks\n * [jekbradbury/qrnn.py](http://metamind.io/research/new-neural-network-building-block-allows-faster-and-more-accurate-text-understanding/) - QRNN\n\n### Computer Vision\n\n * [tscohen/GrouPy](https://github.com/tscohen/GrouPy) - Group Equivariant Convolutional Neural Networks\n * [mitmul/chainer-fast-rcnn](https://github.com/mitmul/chainer-fast-rcnn) - Fast R-CNN\n * [mitmul/chainer-faster-rcnn](https://github.com/mitmul/chainer-faster-rcnn) - Faster R-CNN\n * [mitmul/chainer-cifar10](https://github.com/mitmul/chainer-cifar10) - Cifar10\n * [mitmul/DeepPose](https://github.com/mitmul/deeppose) - Deep pose\n * [mitmul/chainer-conv-vis](https://github.com/mitmul/chainer-conv-vis) - Convolution Filter Visualization\n * [mitmul/chainer-imagenet-vgg](https://github.com/mitmul/chainer-imagenet-vgg) - VGG\n * [mitmul/chainer-segnet](https://github.com/mitmul/chainer-segnet) - SegNet\n * [mitmul/PSPNet](https://github.com/mitmul/chainer-pspnet) - Pyramid Scene Parsing Network\n * [apple2373/chainer-simple-fast-rnn](https://github.com/apple2373/chainer-simple-fast-rnn) - Fast R-CNN\n * [apple2373/chainer_stylenet](https://github.com/apple2373/chainer_stylenet) - StyleNet (A Neural Algorithm of Artistic Style)\n * [apple2373/chainer_caption_generation](https://github.com/apple2373/chainer_caption_generation) - Show and Tell\n * [mrkn/chainer-srcnn](https://github.com/mrkn/chainer-srcnn) - Image super-resolution\n * [Hi-king/chainer_superresolution](https://github.com/Hi-king/chainer_superresolution) - Image super-resolution\n * [darashi/chainer-example-overfeat-classify](https://github.com/darashi/chainer-example-overfeat-classify) - Overfeat\n * [RyotaKatoh/chainer-Variational-AutoEncoder](https://github.com/RyotaKatoh/chainer-Variational-AutoEncoder) - Variational autoencoder (VAE)\n * [yasunorikudo/chainer-ResNet](https://github.com/yasunorikudo/chainer-ResNet) - ResNet\n * [yasunorikudo/chainer-DenseNet](https://github.com/yasunorikudo/chainer-DenseNet) - DenseNet\n * [yasunorikudo/chainer-ResDrop](https://github.com/yasunorikudo/chainer-ResDrop) - ResDrop\n * [yusuketomoto/chainer-fast-neuralstyle](https://github.com/yusuketomoto/chainer-fast-neuralstyle) - Perceptual Losses for Real-Time Style Transfer and Super-Resolution\n * [rezoo/illustration2vec](https://github.com/rezoo/illustration2vec) - illustration2vec\n * [chainer-prednet](https://github.com/kunimasa-kawasaki/chainer-prednet) - Deep Predictive Coding Networks\n * [hillbig/binary_net](https://github.com/hillbig/binary_net) - BinaryNet\n * [stitchfix/fauxtograph](https://github.com/stitchfix/fauxtograph) - Variational Auto-Encoder (VAE), Generative Adversarial Nets (GAN), VAE-GAN\n * [rezoo/data.py](https://gist.github.com/rezoo/4e005611aaa4dad26697) - Generative Adversarial Nets (GAN)\n * [mattya/chainer-gogh](https://github.com/mattya/chainer-gogh) - StyleNet (A Neural Algorithm of Artistic Style)\n * [mattya/chainer-DCGAN](https://github.com/mattya/chainer-DCGAN) - Deep Convolutional Generative Adversarial Network (DCGAN)\n * [mattya/chainer-fluid](https://github.com/mattya/chainer-fluid) - Fluid simulation\n * [ktnyt/chainer_ca.py](https://gist.github.com/ktnyt/58e015dd9ff33049da5a) - Convolutional Autoencoder\n * [tochikuji/chainer-libDNN](https://github.com/tochikuji/chainer-libDNN/blob/master/examples/mnist/SdA.py) - Stacked Denoising Autoencoder\n * [masaki-y/ram](https://github.com/masaki-y/ram) - Recurrent Attention Model\n * [wkentaro/fcn](https://github.com/wkentaro/fcn) - Fully Convolutional Networks\n * [hvy/chainer-gan-denoising-feature-matching](https://github.com/hvy/chainer-gan-denoising-feature-matching) - Generative Adversarial Networks with Denoising Feature Matching\n * [hvy/chainer-visualization](https://github.com/hvy/chainer-visualization) - Visualizing and Understanding Convolutional Networks\n * [hvy/chainer-gan-trainer](https://github.com/hvy/chainer-gan-trainer) - Chainer GAN Trainer\n * [musyoku/wasserstein-gan](https://github.com/musyoku/wasserstein-gan) - WGAN\n * [weihua916/imsat](https://github.com/weihua916/imsat) - IMSAT\n * [Hakuyume/chainer-ssd](https://github.com/Hakuyume/chainer-ssd) - SSD\n * [leetenki/YOLOv2](https://github.com/leetenki/YOLOv2) - YOLOv2\n * [leetenki/YOLOtiny_v2](https://github.com/leetenki/YOLOtiny_v2_chainer) - YOLOtiny_v2\n * [yuyu2172/deformable-conv](https://github.com/yuyu2172/deformable-conv) - Deformable-conv\n * [dsanno/chainer-dfi](https://github.com/dsanno/chainer-dfi) - Deep Feature Interpolation for Image Content Changes\n * [dsanno/chainer-dfi](https://github.com/dsanno/chainer-dfi) - Deep Feature Interpolation for Image Content Changes\n * [osmr/imgclsmob](https://github.com/osmr/imgclsmob) - Collection of classification models pretrained on the ImageNet-1K\n\n### Reinforcement Learning\n\n * [ugo-nama-kun/DQN-chainer](https://github.com/ugo-nama-kun/DQN-chainer) - Deep Q-Network (DQN)\n\n### Generative models\n\n* [crcrpar/chainer-VAE](https://github.com/crcrpar/chainer-VAE) - Variational AutoEncoder\n* [musyoku/LSGAN](https://github.com/musyoku/LSGAN) - LSGAN\n* [musyoku/began](https://github.com/musyoku/began) - BEGAN\n* [musyoku/adversarial-autoencoder](https://github.com/musyoku/adversarial-autoencoder) - adversarial-autoencoder\n* [musyoku/unrolled-gan](https://github.com/musyoku/unrolled-gan) - unrolled-ga\n* [musyoku/improved-gan](https://github.com/musyoku/improved-gan)- improved-gan\n* [musyoku/variational-autoencoder](https://github.com/musyoku/variational-autoencoder) - Semi-Supervised Learning with Deep Generative Models\n* [musyoku/adgm](https://github.com/musyoku/adgm) - Auxiliary Deep Generative Models\n* [musyoku/ddgm](https://github.com/musyoku/ddgm) - Deep Directed Generative Models with Energy-Based Probability Estimation\n* [musyoku/minibatch_discrimination](https://github.com/musyoku/minibatch_discrimination) - Minibatch discrimination\n* [musyoku/wavenet](https://github.com/musyoku/wavenet) - wavenet\n\n### Unsupervised/Semi-supervised learning\n\n* [musyoku/IMSAT](https://github.com/musyoku/IMSAT) - IMSAT\n* [musyoku/vat](https://github.com/musyoku/vat) - VAT\n* [musyoku/mnist-oneshot](https://github.com/musyoku/mnist-oneshot) - mnist-oneshot\n* [mitmul/chainer-siamese](https://github.com/mitmul/chainer-siamese) - Siamese Network\n\n### Others\n\n* [mitmul/chainer-svm](https://github.com/mitmul/chainer-svm) - Support Vector Machine (SVM)\n\n## Blog posts\n\n* [Introduction to Chainer: Neural Networks in Python](http://multithreaded.stitchfix.com/blog/2015/12/09/intro-to-chainer/)\n* [The DIY Guide to Chainer](https://github.com/jxieeducation/DIY-Data-Science/blob/master/frameworks/chainer.md)\n* [CHAINER CHARACTER EMBEDDINGS](http://dirko.github.io/Chainer-character-embeddings/)\n* [A Fontastic Voyage: Generative Fonts with Adversarial Networks](http://multithreaded.stitchfix.com/blog/2016/02/02/a-fontastic-voyage/)\n\n\n## Tools and extensions\n\n* [uei/Deel; A High level deep neural network description language](https://github.com/uei/deel)\n* [uei/DEEPstation](https://libraries.io/github/uei/deepstation)\n* [shi3z/chainer_imagenet_tools](https://github.com/shi3z/chainer_imagenet_tools)\n* [lucidfrontier45/scikit-chainer](https://github.com/lucidfrontier45/scikit-chainer)\n* [tochikuji/chainer-libDNN](https://github.com/tochikuji/chainer-libDNN)\n* [musyoku/weight-normalization](https://github.com/musyoku/weight-normalization) - Weight Normalization Layer for Chainer\n* [musyoku/chainer-sequential](https://github.com/musyoku/chainer-sequential) - chainer-sequential\n* [musyoku/recurrent-batch-normalization](https://github.com/musyoku/recurrent-batch-normalization) - Recurrent Batch Normalization\n* [hitsgub/extra-chainer](https://github.com/hitsgub/extra-chainer) - Implements novel methods\n\n\n\n## Videos\n\n(in Japanese)\n\n* Chainer Beginner's Hands-On: [01 (2018/12/01)](https://www.youtube.com/watch?v=q8LZRdjOkdM), [02 (2018/02/16)](https://www.youtube.com/watch?v=FF_x4G4JuFY)\n* [Chainer の Trainer 解説とNStepLSTM について](https://www.youtube.com/watch?v=ok_bvPKAEaM) Published on Mar 15, 2017\n* [Chainer Meetup #04](https://www.youtube.com/watch?v=Fq5ZQ1ccG38&t=6837s) Published on Feb 23, 2017\n* [1014：深層学習フレームワークChainerの導入と化合物活性予測への応用](https://www.youtube.com/watch?v=lM76gLQag4I&t=1211s) Published on Dec 2, 2015\n\n\n\n## Papers\n\n* [GP-GAN: Towards Realistic High-Resolution Image Blending](https://arxiv.org/abs/1703.07195) \n * Conference: arXiv only \n * Codes: [wuhuikai/GP-GAN](https://github.com/wuhuikai/GP-GAN) \n* [Temporal Generative Adversarial Nets](https://arxiv.org/abs/1611.06624)\n * Conference: arXiv only \n* [Reasoning with Memory Augmented Neural Networks for Language Comprehension](https://arxiv.org/abs/1610.06454)\n * Conference: arXiv only \n* [PMI Matrix Approximations with Applications to Neural Language Modeling](https://arxiv.org/abs/1609.01235)\n * Conference: arXiv only \n* [Neural Tree Indexers for Text Understanding](https://arxiv.org/abs/1607.04492)\n * Conference: arXiv only \n * Codes: [NTI](https://bitbucket.org/tsendeemts/nti/src) \n* [Neural Semantic Encoders](https://arxiv.org/abs/1607.04315)\n * Conference: arXiv only \n* [Networked Intelligence: Towards Autonomous Cyber Physical Systems](https://arxiv.org/abs/1606.04087)\n * Conference: arXiv only \n* [Modeling the dynamics of human brain activity with recurrent neural networks](https://arxiv.org/abs/1606.03071)\n * Conference: arXiv only \n* [A Deep-Learning Approach for Operation of an Automated Realtime Flare Forecast](https://arxiv.org/abs/1606.01587)\n * Conference: arXiv only \n* [Convolutional Neural Networks using Logarithmic Data Representation](https://arxiv.org/abs/1603.01025)\n * Conference: arXiv only \n* [context2vec: Learning Generic Context Embedding with Bidirectional LSTM](http://u.cs.biu.ac.il/%7Emelamuo/publications/context2vec_conll16.pdf)\n * Conference: CoNLL 2016 \n* [Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec](https://arxiv.org/abs/1605.02019)\n * Conference: CoNLL 2016 \n* [Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition](https://arxiv.org/abs/1609.05119) \n * Conference: ECCV 2016 Workshop \n * comments: \"3rd place in Looking at People ECCV Challenge\"\n* [Learning Joint Representations of Videos and Sentences with Web Image Search](https://arxiv.org/abs/1608.02367)\n * Conference: ECCV 2016 Workshop \n* [Incorporating Discrete Translation Lexicons into Neural Machine Translation](https://arxiv.org/abs/1606.02006)\n * Conference: EMNLP 2016 \n* [Controlling Output Length in Neural Encoder-Decoders](https://arxiv.org/abs/1609.09552)\n * Conference: EMNLP 2016 \n* [Insertion Position Selection Model for Flexible Non-Terminals in Dependency Tree-to-Tree Machine Translation](http://www.aclweb.org/anthology/D16-1247)\n * Conference: EMNLP 2016 \n* [Learning Representations Using Complex-Valued Nets](https://arxiv.org/abs/1511.06351)\n * Conference: ICLR 2016 \n* [Dynamic Coattention Networks For Qustion Answering](https://arxiv.org/abs/1611.01604)\n * Conference: ICLR 2017 under review \n* [SqueezeNet: AlexNet-level Accuracy with 50x Fewer Parameters and < 0.5MB Model Size](https://arxiv.org/abs/1602.07360)\n * Conference: ICLR 2017 under review \n* [Quasi-Recurrent Neural Networks](https://arxiv.org/abs/1611.01576)\n * Conference: ICLR 2017 under review \n* [Steerable CNNs](https://arxiv.org/abs/1612.08498)\n * Conference: ICLR 2017 \n * comments: Chainer is not referred in the paper, but the authors kindly informed us.\n* [f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization](https://arxiv.org/abs/1606.00709)\n * Conference: NIPS 2016 Workshop \n* [QSGD: Randomized Quantization for Communication-Optimal Stochastic Gradient Descent](https://arxiv.org/abs/1610.02132)\n * Conference: OPT 2016 \n* [Evaluation of Deep Learning based Pose Estimation for Sign Language Recognition](https://arxiv.org/abs/1602.09065)\n * Conference: PETRA 2016 \n* [Machine-learning Selection of Optical Transients in Subaru/Hyper Suprime-Cam Survey](https://arxiv.org/abs/1609.03249)\n * Conference: PASJ 2016 \n * comments: PASJ: Publications of the Astronomical Society of Japan \n* [A Deep-Learning Approach for Operation of an Automated Realtime Flare Forecast](https://arxiv.org/abs/1606.01587)\n * Conference: Space Weather 2016 \n* [Dynamic Entity Representation with Max-pooling Improves Machine Reading](http://aclweb.org/anthology/N/N16/N16-1099.pdf)\n * Conference: NAACL 2016 \n* [Feature-based Model versus Convolutional Neural Network for Stance Detection](http://aclweb.org/anthology/S/S16/S16-1065.pdf)\n * Conference: SemEval 2016 \n* [Cross-Lingual Image Caption Generation](https://www.aclweb.org/anthology/P/P16/P16-1168.pdf)\n * Conference: ACL 2016 \n* [Composing Distributed Representations of Relational Patterns](http://www.aclweb.org/anthology/P16-1215)\n * Conference: ACL 2016 \n* [Generating Natural Language Descriptions for Semantic Representations of Human Brain Activity](https://www.aclweb.org/anthology/P/P16/P16-3004.pdf)\n * Conference: ACL 2016 \n* [MetaMind Neural Machine Translation System for WMT 2016](https://aclweb.org/anthology/W/W16/W16-2308.pdf)\n * Conference: WMT 2016 \n* [Group Equivariant Convolutional Networks](https://arxiv.org/abs/1602.07576)\n * Conference: ICML 2016 \n * Codes: [GitHub](https://github.com/tscohen/GrouPy) \n * comments: Chainer is not referred in the paper, but the authors kindly informed us.\n* [Robocodes: Towards Generative Street Addresses from Satellite Imagery](https://research.fb.com/publications/robocodes-towards-generative-street-addresses-from-satellite-imagery/)\n * Conference: CVPR 2017 Workshop \n\n\n\n## Official announcements\n\n* [Chainer document](http://docs.chainer.org/en/latest/index.html) - An introduction to Chainer\n* [Chainer blogs](http://chainer.org/blog/)\n\n## Community\n\n* [Introduction to Chainer: Neural Networks in Python](http://multithreaded.stitchfix.com/blog/2015/12/09/intro-to-chainer/)\n* [The DIY Guide to Chainer](https://github.com/jxieeducation/DIY-Data-Science/blob/master/frameworks/chainer.md)\n* [CHAINER CHARACTER EMBEDDINGS](http://dirko.github.io/Chainer-character-embeddings/)\n* [A Fontastic Voyage: Generative Fonts with Adversarial Networks](http://multithreaded.stitchfix.com/blog/2016/02/02/a-fontastic-voyage/)\n\n\n\n\n## Community\n### Global\n\n* [@ChainerOfficial on Twitter](https://twitter.com/ChainerOfficial)\n* [Mailing List](https://groups.google.com/forum/#!forum/chainer)\n* [Slack](http://bit.ly/chainer-slack)\n\n### Japan\n\n* [@ChainerJP on Twitter](https://twitter.com/ChainerJP)\n* [Mailing List](https://groups.google.com/forum/#!forum/chainer-jp)\n* [Slack](http://bit.ly/chainer-jp-slack)\n* [connpass](https://chainer.connpass.com/)\n\n\n\n## Books\n\n* [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\n"
}
]