I recently went to Sheh Zaidi‘s brilliant introduction to Equivariance and Spherical Harmonics and I thought it would be useful to cement my understanding of it with a practical example. In this blog post I’m going to start with serotonin in two coordinate frames, and build a small equivariant neural network that featurises it.
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How to replace bike ball bearings when your steering sounds crunchy
Over the last few months my bicycle steering axle started freezing up, to the point where the first thing I did before getting on my bike in the morning was jerk the handlebars from side to side aggressively to loosen it up. It made atrocious guttural sounds and bangs when I did and navigating Oxford by bike was becoming more treacherous by the day as I swerved from left to right trying to wrestle my front wheel’s fork in the right direction. It was time to undertake some DIY…
Continue readingLaTeX Beamer Template with Logos
Alternative Title: The tragic story of how I got trapped making slides with latex.
Typically after giving a presentation at least one person will approach me and ask if they could have access to my custom latex template to make slides with beamer that don’t look rubbish.
TL;DR Yes you can: https://github.com/npqst/latex-beamer-template
Continue readingGraphormer: Merging GNNs and Transformers for Cheminformatics
This is my first OPIG blog! I’m going to start with a summary of the Graphormer, a Graph Neural Network (GNN) that borrows concepts from Transformers to boost performance on graph tasks. This post is largely based on the NeurIPS paper Do Transformers Really Perform Bad for Graph Representation? by Ying et. al., which introduces the Graphormer, and which we read for our last deep learning journal club. The project has now been integrated as a Microsoft Research project.
I’ll start with a cheap and cheerful summary of Transformers and GNNs before diving into the changes in the Graphormer. Enjoy!
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