124 citations · 499 across the 26 of their papers we have counts for
5 papers · 1 filter
Discrete Denoising Flows
Alexandra Lindt, Emiel Hoogeboom
Discrete flow-based models are a recently proposed class of generative models that learn invertible transformations for discrete random variables. Since they do not require data de…
E(n) Equivariant Normalizing Flows
Victor Garcia Satorras, Emiel Hoogeboom, Fabian B. Fuchs +2
This paper introduces a generative model equivariant to Euclidean symmetries: E(n) Equivariant Normalizing Flows (E-NFs). To construct E-NFs, we take the discriminative E(n) graph…
E(n) Equivariant Graph Neural Networks
Victor Garcia Satorras, Emiel Hoogeboom, Max Welling
This paper introduces a new model to learn graph neural networks equivariant to rotations, translations, reflections and permutations called E(n)-Equivariant Graph Neural Networks…
Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini +2
Generative flows and diffusion models have been predominantly trained on ordinal data, for example natural images. This paper introduces two extensions of flows and diffusion for c…
Variational Determinant Estimation with Spherical Normalizing Flows
Simon Passenheim, Emiel Hoogeboom
This paper introduces the Variational Determinant Estimator (VDE), a variational extension of the recently proposed determinant estimator discovered by arXiv:2005.06553v2. Our esti…