Will Harvey

I am a fourth-year PhD student at the University of British Columbia, and member of the Programming Languages for Artificial Intelligence group led by Frank Wood. Previously I studied for an MEng in Engineering Science at the University of Oxford.

I investigate the intersection of deep learning and Bayesian inference, and use this combination to develop reliable uncertainty estimates for high-dimensional problems (e.g. image completion) that can improve performance in downstream tasks.

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Publications
Conditional Image Generation by Conditioning Variational Auto-Encoders
William Harvey, ,
ICLR 2022


We present conditional VAEs which can be quickly trained by leveraging existing unconditional VAEs. The resulting models provide a more faithful representation of uncertainty than GAN-based approaches with similar training times.

Planning as Inference in Epidemiological Models
, , , , , William Harvey, , , , ,
Frontiers in Artificial Intelligence | Medicine and Public Health 2022


We demonstrate how existing software tools can be used to automate parts of infectious disease-control policy-making via performing inference in existing epidemiological dynamics models.

Assisting the Adversary to Improve GAN Training
, William Harvey,
IJCNN 2021


We improve image quality by training a GAN generator in a way that accounts for a sub-optimal discriminator.

Structured Conditional Continuous Normalizing Flows for Efficient Amortized Inference in Graphical Models
, , William Harvey,
AISTATS 2020


We use knowledge about the structure of a generative model to automatically select a good normalizing flow architecture.

Our new architecture for amortized inference.

Attention for Inference Compilation
William Harvey*, *, , ,
PROBPROG 2020


We present a transformer-based architecture for improved amortized inference in probabilistic programs with complex and stochastic control flow.

Near-Optimal Glimpse Sequences for Improved Hard Attention Neural Network Training
William Harvey, ,
arXiv preprint, 2019


Bayesian experimental design can be used to find near-optimal attention locations for a hard attention mechanism. These can be used to speed up the later training of hard attention mechanisms.

End-to-end Training of Differentiable Pipelines Across Machine Learning Frameworks
, , , William Harvey, , ,
NIPS Autodiff Workshop 2017


We present an interface for gradient-based training of pipelines of machine learning primitives. This allows joint training of machine learning modules written in different languages, making it useful for automated machine learning (AutoML).


Website source forked from Jon Barron.