ToDo: @shunk031さんの運用方法が良さそうなのでそちらの方法に移行する。

• Deep Voice: Real-time Neural Text-to-Speech https://arxiv.org/abs/1702.07825
• Deep Voice 2: Multi-Speaker Neural Text-to-Speech https://research.baidu.com/wp-content/uploads/2017/05/Deep-Voice-2-Complete-Arxiv.pdf
• Fast Wavenet Generation Algorithm https://arxiv.org/abs/1611.09482
• WaveNet: A Generative Model for Raw Audio https://arxiv.org/abs/1609.03499
• Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders https://arxiv.org/abs/1704.01279
• SampleRNN: An Unconditional End-to-End Neural Audio Generation Model https://arxiv.org/abs/1612.07837
• Char2Wav: End-to-End Speech Synthesis https://www.josesotelo.com/speechsynthesis
• Generating Sequences With Recurrent Neural Networks https://arxiv.org/abs/1308.0850
• Tacotron: Towards End-to-End Speech Synthesis https://arxiv.org/abs/1703.10135
• Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks https://www.cs.toronto.edu/~graves/icml_2006.pdf
• Voice Conversion from Unaligned Corpora using Variational Autoencoding Wasserstein Generative Adversarial Networks https://arxiv.org/abs/1704.00849
• Robust speaker-adaptive hmm-based text-to-speech synthesis https://pdfs.semanticscholar.org/8434/758e69ca9674866663bf4fb4a4569e93aaed.pdf
• Voice Conversion Using Sequence-to-Sequence Learning of Context Posterior Probabilities https://arxiv.org/abs/1704.02360
• Sampling-based speech parameter generation using moment-matching networks https://arxiv.org/abs/1704.03626
• Multi-Language Multi-Speaker Acoustic Modeling for LSTM-RNN based Statistical Parametric Speech Synthesis https://research.google.com/pubs/pub45400.html
• Deep Neural Network-Based Speaker Embeddings for End-To-End Speaker Verification https://danielpovey.com/files/2016_slt_xvector.pdf
• End-to-end text-dependent speaker verification https://research.google.com/pubs/pub44681.html
• Exploring Neural Transducers for End-to-End Speech Recognition https://arxiv.org/abs/1707.07413
• Neural Nets for Generating Music https://medium.com/artists-and-machine-intelligence/neural-nets-for-generating-music-f46dffac21c0
• 日本語テキスト音声合成用記号 https://www.jeita.or.jp/cgi-bin/standard/pdf.cgi?jk_n=1408&jk_pdf_file=20110307080703_8FnXHkG4Y0.pdf
• Deep Learning for Siri’s Voice: On-device Deep Mixture Density Networks for Hybrid Unit Selection Synthesis https://machinelearning.apple.com/2017/08/06/siri-voices.html
• Probabilistic acoustic tube: a probabilistic generative model of speech for speech analysis/synthesis https://proceedings.mlr.press/v22/ou12.html
• Investigating the shortcomings of HMM synthesis https://www.cstr.ed.ac.uk/downloads/publications/2013/ssw8_PS2-4_Merritt.pdf
• Generative Adversarial Network-based Postfilter for STFT Spectrograms https://www.kecl.ntt.co.jp/people/kaneko.takuhiro/projects/ganp_stft/index.html
• Deep Learning Techniques for Music Generation - A Survey https://arxiv.org/abs/1709.01620
• Transfer Learning for Speech and Language Processing https://arxiv.org/abs/1511.06066
• Statistical Parametric Speech Synthesis Using Generative Adversarial Networks Under A Multi-task Learning Framework https://arxiv.org/abs/1707.01670
• Statistical Parametric Speech Synthesis Incorporating Generative Adversarial Networks https://arxiv.org/abs/1709.08041
• Statistical Voice Conversion with WaveNet-Based Waveform Generation https://www.isca-speech.org/archive/Interspeech_2017/abstracts/0986.html

• A neural parametric singing synthesizer https://arxiv.org/abs/1704.03809
• Lip Reading Sentences in the Wild https://arxiv.org/abs/1611.05358
• Sequence-to-Sequence Neural Net Models for Grapheme-to-Phoneme Conversion https://arxiv.org/abs/1506.00196
• Listening while Speaking: Speech Chain by Deep Learning https://arxiv.org/abs/1707.04879v1
• Deep Speaker: an End-to-End Neural Speaker Embedding System https://arxiv.org/abs/1705.02304
• EESEN: End-to-End Speech Recognition using Deep RNN Models and WFST-based Decoding https://arxiv.org/abs/1507.08240
• Speech Enhancement Using Bayesian Wavenet https://www.isca-speech.org/archive/Interspeech_2017/abstracts/1672.html

• Pixel Recurrent Neural Networks https://arxiv.org/abs/1601.06759
• PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications https://arxiv.org/abs/1701.05517
• Fast Generation for Convolutional Autoregressive Models https://arxiv.org/abs/1704.06001
• Parallel Multiscale Autoregressive Density Estimation https://arxiv.org/abs/1703.03664
• Qualitatively characterizing neural network optimization problems https://arxiv.org/abs/1412.6544
• Deep Residual Learning for Image Recognition https://arxiv.org/abs/1512.03385
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift https://arxiv.org/abs/1502.03167
• Quasi-Recurrent Neural Networks https://arxiv.org/abs/1611.01576
• Human-level control through deep reinforcement learning https://www.davidqiu.com:8888/research/nature14236.pdf
• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation https://arxiv.org/abs/1406.1078
• Neural Machine Translation by Jointly Learning to Align and Translate https://arxiv.org/abs/1409.0473
• A Neural Transducer https://arxiv.org/abs/1511.04868
• Visualizing Data using t-SNE https://lvdmaaten.github.io/publications/papers/JMLR_2008.pdf https://lvdmaaten.github.io/tsne/
• Attention Is All You Need https://arxiv.org/abs/1706.03762
• Attention and Augmented Recurrent Neural Networks https://distill.pub/2016/augmented-rnns
• Universal Intelligence: A Definition of Machine Intelligence https://arxiv.org/abs/0712.3329
• Convolutional Sequence to Sequence Learning https://arxiv.org/abs/1705.03122
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks https://arxiv.org/abs/1511.06434
• Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space https://arxiv.org/abs/1612.00005
• Wasserstein GAN https://arxiv.org/abs/1701.07875
• One Model To Learn Them All https://arxiv.org/abs/1706.05137
• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation https://arxiv.org/abs/1609.08144
• A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning https://ronan.collobert.com/pub/matos/2008_nlp_icml.pdf
• Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems https://arxiv.org/abs/1508.01745
• Methods for Interpreting and Understanding Deep Neural Networks https://arxiv.org/abs/1706.07979
• End-to-end Continuous Speech Recognition using Attention-based Recurrent NN: First Results https://arxiv.org/abs/1412.1602
• Attention-Based Models for Speech Recognition https://arxiv.org/abs/1506.07503
• Listen, Attend and Spell https://arxiv.org/abs/1508.01211
• Multi-task Sequence to Sequence Learning https://arxiv.org/abs/1511.06114
• Multi-way, multilingual neural machine translation with a shared attention mechanism https://arxiv.org/abs/1601.01073
• Multilingual Language Processing From Bytes https://arxiv.org/abs/1512.00103
• Character-Aware Neural Language Models https://arxiv.org/abs/1508.06615
• Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation https://arxiv.org/abs/1508.02096
• Meta-Learning with Temporal Convolutions https://arxiv.org/abs/1707.03141
• Dual Learning for Machine Translation https://arxiv.org/abs/1611.00179
• FaceNet: A Unified Embedding for Face Recognition and Clustering https://arxiv.org/abs/1503.03832
• Adam: A Method for Stochastic Optimization https://arxiv.org/abs/1412.6980
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling https://arxiv.org/abs/1412.3555
• Grammar as a Foreign Language https://arxiv.org/abs/1412.7449
• Uncertainty in Deep Learning https://mlg.eng.cam.ac.uk/yarin/blog_2248.html
• Understanding Black-box Predictions via Influence Functions https://arxiv.org/abs/1703.04730
• Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses https://arxiv.org/abs/1708.07149
• Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning https://arxiv.org/abs/1702.03274
• Building Machines That Learn and Think Like People https://arxiv.org/abs/1604.00289
• Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models https://arxiv.org/abs/1706.06689
• A Deep Reinforcement Learning Chatbot https://arxiv.org/abs/1709.02349
• Squeeze-and-Excitation Networks https://arxiv.org/abs/1709.01507
• Stochastic Backpropagation and Approximate Inference in Deep Generative Models https://arxiv.org/abs/1401.4082
• Auto-Encoding Variational Bayes https://arxiv.org/abs/1312.6114
• Learning deep architectures for ai https://www.iro.umontreal.ca/~lisa/pointeurs/TR1312.pdf
• From neural PCA to deep unsupervised learning https://arxiv.org/abs/1411.7783
• Deconstructing the Ladder Network Architecture https://arxiv.org/abs/1511.06430
• Neural Optimizer Search with Reinforcement Learning https://arxiv.org/abs/1709.07417
• Deep Predictive Learning: A Comprehensive Model of Three Visual Streams https://arxiv.org/abs/1709.04654
• Poincaré Embeddings for Learning Hierarchical Representations https://arxiv.org/abs/1705.08039

• Conditional Image Generation with PixelCNN Decoders https://arxiv.org/abs/1606.05328
• Generative Adversarial Networks https://arxiv.org/abs/1406.2661
• Sequence to sequence learning with neural networks https://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf
• Highway Networks https://arxiv.org/abs/1505.00387
• Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation https://arxiv.org/abs/1611.04558
• Multi-Source Neural Translation https://arxiv.org/abs/1601.00710
• Fully Character-Level Neural Machine Translation without Explicit Segmentation https://arxiv.org/abs/1610.03017
• Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic Representation Learning https://arxiv.org/abs/1605.03832
• Effective Approaches to Attention-based Neural Machine Translation https://arxiv.org/abs/1508.04025
• Long Short-Term Memory https://web.eecs.utk.edu/~itamar/courses/ECE-692/Bobby_paper1.pdf
• On the Properties of Neural Machine Translation: Encoder-Decoder Approaches https://arxiv.org/abs/1409.1259
• Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks https://arxiv.org/abs/1506.03099
• Twin Networks: Using the Future as a Regularizer https://arxiv.org/abs/1708.06742
• Learning Hierarchical Features from Generative Models https://arxiv.org/abs/1702.08396
• Improving neural networks by preventing co-adaptation of feature detectors https://arxiv.org/abs/1207.0580

• Visualizing and Understanding Neural Machine Translation https://aclanthology.coli.uni-saarland.de/pdf/P/P17/P17-1106.pdf
• Weighted Finite-State Transducers in Speech Recognition https://www.openfst.org/twiki/pub/FST/FstBackground/csl01.pdf
• SPEECH RECOGNITION WITH WEIGHTED FINITE-STATE TRANSDUCERS https://www.openfst.org/twiki/pub/FST/FstBackground/hbka.pdf
• Weighted Automata Algorithms https://www.cs.nyu.edu/~mohri/pub/hwa.pdf
• OpenFst: A General and Efficient Weighted Finite-State Transducer Library https://www.openfst.org/twiki/pub/FST/FstBackground/ciaa.pdf
• Natural Language Processing (almost) from Scratch https://www.jmlr.org/papers/volume12/collobert11a/collobert11a.pdf

• Probabilistic Typology: Deep Generative Models of Vowel Inventories https://arxiv.org/abs/1705.01684

• The Google File System https://research.google.com/archive/gfs.html
• Dynamo: Amazon’s Highly Available Key-value Store https://www.allthingsdistributed.com/2007/10/amazons_dynamo.html
• Bigtable: A Distributed Storage System for Structured Data https://research.google.com/archive/bigtable.html
• Simple Testing Can Prevent Most Critical Failures- An Analysis of Production Failures in Distributed Data-Intensive Systems https://www.usenix.org/system/files/conference/osdi14/osdi14-paper-yuan.pdf
• A comprehensive study of Convergent and Commutative Replicated Data Types https://hal.inria.fr/file/index/docid/555588/filename/techreport.pdf
• MapReduce: Simplified Data Processing on Large Clusters https://research.google.com/archive/mapreduce.html
• HAT, not CAP: Towards Highly Available Transactions https://www.usenix.org/system/files/conference/hotos13/hotos13-final80.pdf
• Highly Available Transactions: Virtues and Limitations https://arxiv.org/abs/1302.0309
• Use of Formal Methods at Amazon Web Services https://lamport.azurewebsites.net/tla/amazon.html
• Eventually Consistent - Revisited https://www.allthingsdistributed.com/2008/12/eventually_consistent.html
• Borg, Omega, and Kubernetes https://research.google.com/pubs/pub44843.html
• Design patterns for container-based distributed systems https://research.google.com/pubs/pub45406.html