39 citations · 45 across the 3 of their papers we have counts for
8 papers
Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent
Priyank Jaini, Lars Holdijk, Max Welling
We focus on the problem of efficient sampling and learning of probability densities by incorporating symmetries in probabilistic models. We first introduce Equivariant Stein Variat…
Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC
Priyank Jaini, Didrik Nielsen, Max Welling
Hybrid Monte Carlo is a powerful Markov Chain Monte Carlo method for sampling from complex continuous distributions. However, a major limitation of HMC is its inability to be appli…
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…
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:…
A Positivstellensatz for Conditional SAGE Signomials
Allen Houze Wang, Priyank Jaini, Yaoliang Yu +1
Recently, the conditional SAGE certificate has been proposed as a sufficient condition for signomial positivity over a convex set. In this article, we show that the conditional SAG…