117 citations · 456 across the 12 of their papers we have counts for
12 papers
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…
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…
Dynamic Evaluation of Transformer Language Models
Ben Krause, Emmanuel Kahembwe, Iain Murray +1
This research note combines two methods that have recently improved the state of the art in language modeling: Transformers and dynamic evaluation. Transformers use stacked layers…
BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning
Asa Cooper Stickland, Iain Murray
Multi-task learning shares information between related tasks, sometimes reducing the number of parameters required. State-of-the-art results across multiple natural language unders…