334 citations · 2.2k across the 37 of their papers we have counts for
38 papers
Maximum Likelihood Training of Score-Based Diffusion Models
Yang Song, Conor Durkan, Iain Murray +1
Score-based diffusion models synthesize samples by reversing a stochastic process that diffuses data to noise, and are trained by minimizing a weighted combination of score matchin…
Density Deconvolution with Normalizing Flows
Tim Dockhorn, James A. Ritchie, Yaoliang Yu +1
Density deconvolution is the task of estimating a probability density function given only noise-corrupted samples. We can fit a Gaussian mixture model to the underlying density by…
Diverse Ensembles Improve Calibration
Asa Cooper Stickland, Iain Murray
Modern deep neural networks can produce badly calibrated predictions, especially when train and test distributions are mismatched. Training an ensemble of models and averaging thei…
Ordering Dimensions with Nested Dropout Normalizing Flows
Artur Bekasov, Iain Murray
The latent space of normalizing flows must be of the same dimensionality as their output space. This constraint presents a problem if we want to learn low-dimensional, semantically…
On Contrastive Learning for Likelihood-free Inference
Conor Durkan, Iain Murray, George Papamakarios
Likelihood-free methods perform parameter inference in stochastic simulator models where evaluating the likelihood is intractable but sampling synthetic data is possible. One class…
Scalable Extreme Deconvolution
James A. Ritchie, Iain Murray
The Extreme Deconvolution method fits a probability density to a dataset where each observation has Gaussian noise added with a known sample-specific covariance, originally intende…