Priyank Jaini | DeepAI

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∙ 03/27/2023

Text-to-Image Diffusion Models are Zero-Shot Classifiers

The excellent generative capabilities of text-to-image diffusion models ...

0 Kevin Clark, et al. ∙

∙ 11/26/2021

Particle Dynamics for Learning EBMs

Energy-based modeling is a promising approach to unsupervised learning, ...

1 Kirill Neklyudov, et al. ∙

∙ 06/15/2021

Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent

We focus on the problem of efficient sampling and learning of probabilit...

5 Priyank Jaini, et al. ∙

∙ 02/10/2021

Argmax Flows and Multinomial Diffusion: Towards Non-Autoregressive Language Models

The field of language modelling has been largely dominated by autoregres...

2 Emiel Hoogeboom, et al. ∙

∙ 02/04/2021

Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC

Hybrid Monte Carlo is a powerful Markov Chain Monte Carlo method for sam...

11 Priyank Jaini, et al. ∙

∙ 11/14/2020

Self Normalizing Flows

Efficient gradient computation of the Jacobian determinant term is a cor...

7 T. Anderson Keller, et al. ∙

∙ 07/06/2020

SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows

Normalizing flows and variational autoencoders are powerful generative m...

12 Didrik Nielsen, et al. ∙

∙ 03/08/2020

Complete Hierarchy of Relaxation for Constrained Signomial Positivity

In this article, we prove that the Sums-of-AM/GM Exponential (SAGE) rela...

0 Allen Houze Wang, et al. ∙

∙ 07/10/2019

Tails of Triangular Flows

Triangular maps are a construct in probability theory that allows the tr...

2 Priyank Jaini, et al. ∙

∙ 05/07/2019

Sum-of-Squares Polynomial Flow

Triangular map is a recent construct in probability theory that allows o...

0 Priyank Jaini, et al. ∙

∙ 09/19/2016

Online and Distributed learning of Gaussian mixture models by Bayesian Moment Matching

The Gaussian mixture model is a classic technique for clustering and dat...

0 Priyank Jaini, et al. ∙

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