暂无分享,去创建一个
[1] Xi Chen,et al. PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications , 2017, ICLR.
[2] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[3] Jason Lee,et al. Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement , 2018, EMNLP.
[4] Noah Constant,et al. Character-Level Language Modeling with Deeper Self-Attention , 2018, AAAI.
[5] Aaron C. Courville,et al. Recurrent Batch Normalization , 2016, ICLR.
[6] Alexandre Lacoste,et al. Neural Autoregressive Flows , 2018, ICML.
[7] Richard E. Turner,et al. Neural Adaptive Sequential Monte Carlo , 2015, NIPS.
[8] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[9] E. Nering,et al. Linear Algebra and Matrix Theory , 1964 .
[10] Zhiting Hu,et al. Improved Variational Autoencoders for Text Modeling using Dilated Convolutions , 2017, ICML.
[11] E. Tabak,et al. DENSITY ESTIMATION BY DUAL ASCENT OF THE LOG-LIKELIHOOD ∗ , 2010 .
[12] Victor O. K. Li,et al. Non-Autoregressive Neural Machine Translation , 2017, ICLR.
[13] Uri Shalit,et al. Structured Inference Networks for Nonlinear State Space Models , 2016, AAAI.
[14] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[15] Prafulla Dhariwal,et al. Glow: Generative Flow with Invertible 1x1 Convolutions , 2018, NeurIPS.
[16] Yoshua Bengio,et al. Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.
[17] Samy Bengio,et al. Density estimation using Real NVP , 2016, ICLR.
[18] Hugo Larochelle,et al. MADE: Masked Autoencoder for Distribution Estimation , 2015, ICML.
[19] Zhe Gan,et al. Deep Temporal Sigmoid Belief Networks for Sequence Modeling , 2015, NIPS.
[20] Alexander M. Rush,et al. Semi-Amortized Variational Autoencoders , 2018, ICML.
[21] Shakir Mohamed,et al. Variational Inference with Normalizing Flows , 2015, ICML.
[22] Ole Winther,et al. Sequential Neural Models with Stochastic Layers , 2016, NIPS.
[23] Max Welling,et al. Improved Variational Inference with Inverse Autoregressive Flow , 2016, NIPS 2016.
[24] Pieter Abbeel,et al. Variational Lossy Autoencoder , 2016, ICLR.
[25] Lior Wolf,et al. Using the Output Embedding to Improve Language Models , 2016, EACL.
[26] Matthias W. Seeger,et al. Deep State Space Models for Time Series Forecasting , 2018, NeurIPS.
[27] G. C. Holmes. The use of hyperbolic cosines in solving cubic polynomials , 2002, The Mathematical Gazette.
[28] Heiga Zen,et al. Parallel WaveNet: Fast High-Fidelity Speech Synthesis , 2017, ICML.
[29] Yoshua Bengio,et al. A Recurrent Latent Variable Model for Sequential Data , 2015, NIPS.
[30] Jiacheng Xu,et al. Spherical Latent Spaces for Stable Variational Autoencoders , 2018, EMNLP.
[31] Aurko Roy,et al. Fast Decoding in Sequence Models using Discrete Latent Variables , 2018, ICML.
[32] Yoshua Bengio,et al. Z-Forcing: Training Stochastic Recurrent Networks , 2017, NIPS.
[33] Christian Osendorfer,et al. Learning Stochastic Recurrent Networks , 2014, NIPS 2014.
[34] Richard Socher,et al. An Analysis of Neural Language Modeling at Multiple Scales , 2018, ArXiv.
[35] Samy Bengio,et al. Generating Sentences from a Continuous Space , 2015, CoNLL.
[36] Iain Murray,et al. Masked Autoregressive Flow for Density Estimation , 2017, NIPS.
[37] Thomas Hofmann,et al. Deep State Space Models for Unconditional Word Generation , 2018, NeurIPS.
[38] Yoshua Bengio,et al. Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription , 2012, ICML.
[39] Ilya Sutskever,et al. SUBWORD LANGUAGE MODELING WITH NEURAL NETWORKS , 2011 .