暂无分享,去创建一个
[1] Ole Winther,et al. Auxiliary Deep Generative Models , 2016, ICML.
[2] Yoshua Bengio,et al. A Recurrent Latent Variable Model for Sequential Data , 2015, NIPS.
[3] Oriol Vinyals,et al. Neural Discrete Representation Learning , 2017, NIPS.
[4] Alex Graves,et al. Generating Sequences With Recurrent Neural Networks , 2013, ArXiv.
[5] Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.
[6] Zhiting Hu,et al. Improved Variational Autoencoders for Text Modeling using Dilated Convolutions , 2017, ICML.
[7] Yoshua Bengio,et al. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.
[8] Samy Bengio,et al. Generating Sentences from a Continuous Space , 2015, CoNLL.
[9] Alexander M. Rush,et al. Avoiding Latent Variable Collapse With Generative Skip Models , 2018, AISTATS.
[10] David Vázquez,et al. PixelVAE: A Latent Variable Model for Natural Images , 2016, ICLR.
[11] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[12] Ole Winther,et al. Ladder Variational Autoencoders , 2016, NIPS.
[13] Guokun Lai,et al. Stochastic WaveNet: A Generative Latent Variable Model for Sequential Data , 2018, ArXiv.
[14] Vladlen Koltun,et al. Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.
[15] Yoshua Bengio,et al. Variational Bi-LSTMs , 2017, ArXiv.
[16] Stefano Ermon,et al. Learning Hierarchical Features from Generative Models , 2017, ArXiv.
[17] Ole Winther,et al. Sequential Neural Models with Stochastic Layers , 2016, NIPS.
[18] Diederik P. Kingma,et al. Variational Recurrent Auto-Encoders , 2014, ICLR.
[19] Yann Dauphin,et al. Language Modeling with Gated Convolutional Networks , 2016, ICML.
[20] Yuan Yu,et al. TensorFlow: A system for large-scale machine learning , 2016, OSDI.
[21] Otmar Hilliges,et al. DeepWriting: Making Digital Ink Editable via Deep Generative Modeling , 2018, CHI.
[22] Christian Osendorfer,et al. Learning Stochastic Recurrent Networks , 2014, NIPS 2014.
[23] Vladlen Koltun,et al. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling , 2018, ArXiv.
[24] Yoshua Bengio,et al. Professor Forcing: A New Algorithm for Training Recurrent Networks , 2016, NIPS.
[25] Yee Whye Teh,et al. Filtering Variational Objectives , 2017, NIPS.
[26] Alex Graves,et al. Conditional Image Generation with PixelCNN Decoders , 2016, NIPS.
[27] Philip Bachman,et al. An Architecture for Deep, Hierarchical Generative Models , 2016, NIPS.
[28] Koray Kavukcuoglu,et al. Pixel Recurrent Neural Networks , 2016, ICML.
[29] Karen Simonyan,et al. The challenge of realistic music generation: modelling raw audio at scale , 2018, NeurIPS.
[30] Ruslan Salakhutdinov,et al. Importance Weighted Autoencoders , 2015, ICLR.
[31] Yann Dauphin,et al. Convolutional Sequence to Sequence Learning , 2017, ICML.
[32] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[33] Yoshua Bengio,et al. Z-Forcing: Training Stochastic Recurrent Networks , 2017, NIPS.
[34] Heiga Zen,et al. WaveNet: A Generative Model for Raw Audio , 2016, SSW.
[35] Aurko Roy,et al. Fast Decoding in Sequence Models using Discrete Latent Variables , 2018, ICML.
[36] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).