124 citations · 499 across the 26 of their papers we have counts for
5 papers · 1 filter
Self Normalizing Flows
T. Anderson Keller, Jorn W. T. Peters, Priyank Jaini +3
Efficient gradient computation of the Jacobian determinant term is a core problem in many machine learning settings, and especially so in the normalizing flow framework. Most propo…
SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows
Didrik Nielsen, Priyank Jaini, Emiel Hoogeboom +2
Normalizing flows and variational autoencoders are powerful generative models that can represent complicated density functions. However, they both impose constraints on the models:…
The Convolution Exponential and Generalized Sylvester Flows
Emiel Hoogeboom, Victor Garcia Satorras, Jakub M. Tomczak +1
This paper introduces a new method to build linear flows, by taking the exponential of a linear transformation. This linear transformation does not need to be invertible itself, an…
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…