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Normalizing flows are a powerful class of generative models for continuous random variables, showing both strong model flexibility and the potential for non-autoregressive generation.
Linear Algebra and Matrix Theory
Nering, E. D · 1970
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The Use of Hyperbolic Cosines in Solving Cubic Polynomials
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Density estimation by dual ascent of the log-likelihood
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Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription
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Subword language modeling with neural networks
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MADE: Masked Autoencoder for Distribution Estimation
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Variational Inference with Normalizing Flows
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Generating Sentences from a Continuous Space
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A Recurrent Latent Variable Model for Sequential Data
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Variational Lossy Autoencoder
Chen, X., Kingma, D. P., Salimans, T., Duan, Y., Dhariwal, P., Schulman, J., Sutskever, I., and Abbeel, P · 2017
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Attention Is All You Need
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Improved Variational Autoencoders for Text Modeling using Dilated Convolutions
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Non-Autoregressive Neural Machine Translation
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Huang, C.-W., Krueger, D., Lacoste, A., and Courville, A · 2018
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Fast Decoding in Sequence Models Using Discrete Latent Variables
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Semi-Amortized Variational Autoencoders
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Density estimation using Real NVP
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