3.6k citations · 4.1k across the 6 of their papers we have counts for
5 papers
Step-unrolled Denoising Autoencoders for Text Generation
Nikolay Savinov, Junyoung Chung, Mikolaj Binkowski +2
In this paper we propose a new generative model of text, Step-unrolled Denoising Autoencoder (SUNDAE), that does not rely on autoregressive models. Similarly to denoising diffusion…
Generating Diverse High-Fidelity Images with VQ-VAE-2
Ali Razavi, Aaron van den Oord, Oriol Vinyals
We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation. To this end, we scale and enhance the autoregressive priors used in…
Video Pixel Networks
Nal Kalchbrenner, Aaron van den Oord, Karen Simonyan +4
We propose a probabilistic video model, the Video Pixel Network (VPN), that estimates the discrete joint distribution of the raw pixel values in a video. The model and the neural a…
Neural Machine Translation in Linear Time
Nal Kalchbrenner, Lasse Espeholt, Karen Simonyan +3
We present a novel neural network for processing sequences. The ByteNet is a one-dimensional convolutional neural network that is composed of two parts, one to encode the source se…
WaveNet: A Generative Model for Raw Audio
Aaron van den Oord, Sander Dieleman, Heiga Zen +6
This paper introduces WaveNet, a deep neural network for generating raw audio waveforms. The model is fully probabilistic and autoregressive, with the predictive distribution for e…