190 citations · 633 across the 32 of their papers we have counts for
34 papers
Generalization Gap in Amortized Inference
Mingtian Zhang, Peter Hayes, David Barber
The ability of likelihood-based probabilistic models to generalize to unseen data is central to many machine learning applications such as lossless compression. In this work, we st…
Adaptive Optimization with Examplewise Gradients
Julius Kunze, James Townsend, David Barber
We propose a new, more general approach to the design of stochastic gradient-based optimization methods for machine learning. In this new framework, optimizers assume access to a b…
Sample Efficient Model Evaluation
Emine Yilmaz, Peter Hayes, Raza Habib +2
Labelling data is a major practical bottleneck in training and testing classifiers. Given a collection of unlabelled data points, we address how to select which subset to label to…
Locally-Contextual Nonlinear CRFs for Sequence Labeling
Harshil Shah, Tim Xiao, David Barber
Linear chain conditional random fields (CRFs) combined with contextual word embeddings have achieved state of the art performance on sequence labeling tasks. In many of these tasks…
Learning Deep-Latent Hierarchies by Stacking Wasserstein Autoencoders
Benoit Gaujac, Ilya Feige, David Barber
Probabilistic models with hierarchical-latent-variable structures provide state-of-the-art results amongst non-autoregressive, unsupervised density-based models. However, the most…
Learning disentangled representations with the Wasserstein Autoencoder
Benoit Gaujac, Ilya Feige, David Barber
Disentangled representation learning has undoubtedly benefited from objective function surgery. However, a delicate balancing act of tuning is still required in order to trade off…