research
∙
06/15/2022
Diffusion Models for Video Prediction and Infilling
To predict and anticipate future outcomes or reason about missing inform...
share
research
∙
05/30/2022
Few-Shot Diffusion Models
Denoising diffusion probabilistic models (DDPM) are powerful hierarchica...
research
∙
02/10/2021
Argmax Flows and Multinomial Diffusion: Towards Non-Autoregressive Language Models
The field of language modelling has been largely dominated by autoregres...
research
∙
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...
research
∙
07/06/2020
SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows
Normalizing flows and variational autoencoders are powerful generative m...
research
∙
02/06/2020
Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow
Flow models have recently made great progress at modeling quantized sens...
research
∙
11/11/2018
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient
Uncertainty estimation in large deep-learning models is a computationall...
research
∙
07/12/2018
Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex Models
Bayesian inference plays an important role in advancing machine learning...
research
∙
06/13/2018
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
Uncertainty computation in deep learning is essential to design robust a...
research
∙
11/15/2017