Graph Convolutional Neural Networks

As more and more businesses strive toward becoming data-driven, the use of graph methods for storing relational data has been on the rise ( [1], [2], [3]). Along with these graph databases comes more opportunities for analysing the data, including the use of predictive machine learning models on graphs. [Read More]

Doubt - Bringing back uncertainty to ML

I have previously been exploring uncertainty measures that we can build into our machine learning models, making it easier to see whether a concrete prediction can be trusted or not. This involved confidence intervals for datasets and prediction intervals for models; see the previous posts in this series for a... [Read More]

DeepWalk

Deep learning has almost exclusively been working with simple objects: images and text. By simple I am here referring to the graphical structure of these objects, where a word is a linear sequence of letters, a document is a linear sequence of words, and an image is a rectangular grid... [Read More]

The PageRank Algorithm

I’ve recently started working with graph structures in the context of machine learning, and have found that I’ve opened what seems to be a reverse Pandora’s box, full of neat algorithms that can pull out a lot of insights from graph structures. As a way of cementing my knowledge and... [Read More]

Quantile regression forests

A random forest is an incredibly useful and versatile tool in a data scientist’s toolkit, and is one of the more popular non-deep models that are being used in industry today. If we now want our random forests to also output their uncertainty, it would seem that we are forced... [Read More]