research
∙
02/27/2023
Denoising Diffusion Samplers
Denoising diffusion models are a popular class of generative models prov...
share
research
∙
02/22/2023
Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC
Since their introduction, diffusion models have quickly become the preva...
research
∙
11/28/2022
Continuous diffusion for categorical data
Diffusion models have quickly become the go-to paradigm for generative m...
research
∙
11/08/2022
Self-conditioned Embedding Diffusion for Text Generation
Can continuous diffusion models bring the same performance breakthrough ...
research
∙
10/31/2022
Learning to Navigate Wikipedia by Taking Random Walks
A fundamental ability of an intelligent web-based agent is seeking out a...
research
∙
08/16/2022
Score-Based Diffusion meets Annealed Importance Sampling
More than twenty years after its introduction, Annealed Importance Sampl...
research
∙
02/08/2021
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions
We propose a general and scalable approximate sampling strategy for prob...
research
∙
10/08/2020
No MCMC for me: Amortized sampling for fast and stable training of energy-based models
Energy-Based Models (EBMs) present a flexible and appealing way to repre...
research
∙
02/13/2020
Cutting out the Middle-Man: Training and Evaluating Energy-Based Models without Sampling
We present a new method for evaluating and training unnormalized density...
research
∙
12/06/2019
Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One
We propose to reinterpret a standard discriminative classifier of p(y|x)...
research
∙
10/02/2018
FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models
A promising class of generative models maps points from a simple distrib...
research
∙
12/14/2016