Deeplearning Papers (2016.11.9 ~)

Deep learning paper list (ongoing)

ICLR 2017 OpenReview

• Yaniv Taigman, Adam Polyak, Lior Wolf. Unsupervised Cross-Domain Image Generation (2016.11) [[ICLR 2017 open review]] (http://104.155.136.4:3000/forum?id=Sk2Im59ex)

Google Brain Team

• Konstantinos Bousmalis et al. Domain Separation Networks (NIPS 2016) [[arXiv]] (https://arxiv.org/abs/1608.06019)

• Amit Daniely et al. Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity (2016.2) [[arXiv]] (https://arxiv.org/abs/1602.05897)

• S. M. Ali Eslami et al. Attend, Infer, Repeat: Fast Scene Understanding with Generative Models (2016.3) [[arXiv]] (https://arxiv.org/abs/1603.08575)

• Jimmy Ba et al. Using Fast Weights to Attend to the Recent Past (2016.10) [arXiv]

DeepMind

• M Fraccaro et al. Sequential Neural Models with Stochastic Layers (NIPS 2016) [[arXiv]] (https://arxiv.org/abs/1605.07571)

• M Andrychowicz et al. Learning to Learn by Gradient Descent by Gradient Descent (NIPS 2016) [[arXiv]] (https://arxiv.org/abs/1606.04474)

• Max Jaderberg et al. Decoupled Neural Interfaces using Synthetic Gradients (2016.8) [[arXiv]] (https://arxiv.org/abs/1608.05343)

• A van den Oord et al WaveNet: A Generative Model for Raw Audio (2016.9) [[arXiv]] (https://arxiv.org/abs/1609.03499)

Facebook AI Research (FAIR)

• Pauline Luc et al. Semantic Segmentation using Adversarial Networks (2016.11) [[arXiv]] (https://arxiv.org/abs/1611.08408)

• Xianming Liu et al. Feedback Neural Network for Weakly Supervised Geo-Semantic Segmentation (2016.12) [[arXiv]] (https://arxiv.org/abs/1612.02766)

Model

• Sergey Ioffe, Christian Szegedy. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (2015.11) [[arXiv]] (https://arxiv.org/abs/1502.03167)

• Tim Cooijmans et al. Recurrent Batch Normalization (2016.3) [[arXiv]] (https://arxiv.org/abs/1603.09025)

• Jimmy Ba et al. Layer Normalization (2016.7) [[arXiv]] (https://arxiv.org/abs/1607.06450)

• David Krueger et al. Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations (2016.11) [[ICLR 2017 open review]] (http://104.155.136.4:3000/forum?id=rJqBEPcxe)

• Gabriel Pereyra et al. Regularizing Neural Networks by Penalizing Confident Output Distributions (2016.11) [[ICLR 2017 open review]] (http://104.155.136.4:3000/forum?id=HkCjNI5ex)

• Yingce Xia et al. Dual Learning for Machine Translation (2016.11) [[arXiv]] (https://arxiv.org/abs/1611.00179)

Generative Model

• Diederik P Kingma, Max Welling. Auto-Encoding Variational Bayes (2013.12) [[arXiv]] (https://arxiv.org/abs/1312.6114)

• Ian J. Goodfellow et al. Generative Adversarial Networks (2014.6) [[arXiv]] (https://arxiv.org/abs/1406.2661)

• Alec Radford, Luke Metz, Soumith Chintala. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks (2015.11) [[arXiv]] (https://arxiv.org/abs/1511.06434)

Sequence-to-Sequence Model

• Kyunghyun Cho et al. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. (2014.6) [arXiv] [[notes]] (https://github.com/yunjey/deeplearning-papers/blob/master/notes/learning_phrase_representation_using_rnn_enc_dec.md)

• Ilya Sutskever, Oriol Vinyals, Quoc V. Le. Sequence to Sequence Learning with Neural Networks. (2014.9) [arXiv] [[notes]] (https://github.com/yunjey/deeplearning-papers/blob/master/notes/seq2seq_with_nn.md)

• Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio. Neural Machine Translation by Jointly Learning to Align and Translate. (2014.9) [arXiv] [[notes]] (https://github.com/yunjey/deeplearning-papers/blob/master/notes/nmt_by_jointly_train_align_and_translate.md)

Image Captioning

• Oriol Vinyals et al. Show and Tell: A Neural Image Caption Generator (2014.11) [[arXiv]] (https://arxiv.org/abs/1411.4555)

• Xu Kelvin et al. Show, attend and tell: Neural image caption generation with visual attention". (2015.2) [[arXiv]] (https://arxiv.org/abs/1502.03044) [[notes]] (https://github.com/yunjey/deeplearning-papers/blob/master/notes/show_attend_and_tell.md) [[tensorflow]] (https://github.com/yunjey/show-attend-and-tell-tensorflow)

• Oriol Vinyals et al. Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge (2016.9) [[arXiv]] (https://arxiv.org/abs/1609.06647) [[tensorflow]] (https://github.com/tensorflow/models/tree/master/im2txt)

• Jiasen Lu et al. Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning (2016.12) [[arXiv]] (https://arxiv.org/abs/1612.01887)

Natural Language Processing

• Jason P.C. Chiu, Eric Nichols. Named Entity Recognition with Bidirectional LSTM-CNNs (2015.11) (ACL 2016) [[arXiv]] (https://arxiv.org/abs/1511.08308)

• Jianpeng Cheng, Mirella Lapata. Neural Summarization by Extracting Sentences and Words (2016.3) (ACL 2016) [[arXiv]] (https://arxiv.org/abs/1603.07252)

• Iulian Vlad Serban et al. Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus (2016.3) (ACL 2016) [[arXiv]] (https://arxiv.org/abs/1603.06807) [[notes]] (https://github.com/yunjey/deeplearning-papers/blob/master/notes/generating_factoid_questions_with_rnn.md)

• Karl Pichotta, Raymond J. Mooney. Using Sentence-Level LSTM Language Models for Script Inference (2016.8) (ACL 2016) [[arXiv]] (https://arxiv.org/abs/1604.02993)

• Yunchuan Chen et al. Compressing Neural Language Models by Sparse Word Representations (2016.10) (ACL 2016) [[arXiv]] (https://arxiv.org/abs/1610.03950)