43 citations · 59 across the 2 of their papers we have counts for
4 papers
Predictive Sampling with Forecasting Autoregressive Models
Auke Wiggers, Emiel Hoogeboom
Autoregressive models (ARMs) currently hold state-of-the-art performance in likelihood-based modeling of image and audio data. Generally, neural network based ARMs are designed to…
Learning Discrete Distributions by Dequantization
Emiel Hoogeboom, Taco S. Cohen, Jakub M. Tomczak
Media is generally stored digitally and is therefore discrete. Many successful deep distribution models in deep learning learn a density, i.e., the distribution of a continuous ran…
Integer Discrete Flows and Lossless Compression
Emiel Hoogeboom, Jorn W. T. Peters, Rianne van den Berg +1
Lossless compression methods shorten the expected representation size of data without loss of information, using a statistical model. Flow-based models are attractive in this setti…
Emerging Convolutions for Generative Normalizing Flows
Emiel Hoogeboom, Rianne van den Berg, Max Welling
Generative flows are attractive because they admit exact likelihood optimization and efficient image synthesis. Recently, Kingma & Dhariwal (2018) demonstrated with Glow that gener…