The Erlangen Programme of ML

This blog post was co-authored with Joan Bruna, Taco Cohen, and Petar Veličković and is based on the new “proto-book” M. M. Bronstein, J. Bruna, T. Cohen, and P. …

Thoughts and Theory, Rethinking GNNs

This blog post was co-authored with Ben Chamberlain and James Rowbottom, and is based on our paper B. Chamberlain, J. Rowbottom et al., GRAND: Graph Neural Diffusion (2021) ICML.

First page of “Scala graduum Caloris”, a 1701 paper by Sir Isaac Newton published anonymously in the Philosophical Transactions of the Royal Society. Shown is a temperature scale with 0 corresponding to the temperature of “winter air when water starts freezing” (aqua incipit gelu rigescere) and 12 representing the temperature measured upon “contact with the human body” (contactum corporis humani). The highest temperature of 210 is that of “kitchen fire urged by bellows”.

In March 1701, the Philosophical Transactions of the Royal Society published an anonymous note in Latin titled “A Scale of the…

Year 2020 in Review & Predictions for 2021

Image: Shutterstock

Beyond message passing

Will Hamilton, Assistant Professor at McGill University and CIFAR Chair at Mila, author of GraphSAGE.

“2020 saw the field of Graph ML come to terms with the fundamental limitations of the message-passing paradigm.

These limitations include the so-called “bottleneck” issue [1], problems with over-smoothing [2], and theoretical limits in terms…

The best of Graph Deep Learning in 2020

Geometric ML methods were featured on the covers of the February 2020 issues of two major biology magazines, Cell and Nature Methods. Image credits: Amanda Cicero, Luca Vallescura, Darryl “Moose” Norris, and Chris Sinclair (left) and Laura Persat and Erin Dewalt (right).

J. M. Stokes et al., A deep learning approach to antibiotic discovery (2020) Cell 180(4):688–702.

What? A graph neural network-based deep learning pipeline for the discovery of new antibiotic drugs.

How? A graph neural network was trained to predict the growth inhibition of the bacterium Escherichia coli on a dataset…

Making Sense of Big Data, Recommender Systems 2020 challenge

This blog post was co-authored with Luca Belli, Apoorv Sharma, Yuanpu Xie, Ying Xiao, Dan Shiebler, Max Hansmire, and Wenzhe Shi from Twitter Cortex.

Recommender systems are an important part of modern social networks and e-commerce platforms. They aim to maximise user satisfaction as well as other key business objectives…

Let food be thy medicine

Illustration: Bianca Dagheti.

This post was co-authored with Kirill Veselkov and Gabriella Sbordone and is based on the TEDx Lugano 2019 talk and the paper published in Nature journal Scientific Reports.

We now live longer than ever. Yet, we are not necessarily living healthier anymore: with a rapidly aging population, people are experiencing…

Deep learning with latent graphs

The past few years have witnessed a surge of interest in developing ML methods for graph-structured data. Such data naturally arises in many applications such as social sciences (e.g. the Follow graph of users on Twitter or Facebook), chemistry (where molecules can be modelled as graphs of atoms connected by…

Deep learning on giant graphs

This post was co-authored with Fabrizo Frasca and Emanuele Rossi.

Graph Neural Networks (GNNs) are a class of ML models that have emerged in recent years for learning on graph-structured data. GNNs have been successfully applied to model systems of relation and interactions in a variety of different domains, including…

Deep learning on dynamic graphs

This post was co-authored with Emanuele Rossi.

A dynamic network of Twitter users interacting with tweets and following each other. All the edges have a timestamp. Given such a dynamic graph, we want to predict future interactions, e.g., which tweet a user will like or whom they will follow.

Graph neural networks (GNNs) research has surged to become one of the hottest topics in machine learning this year. GNNs have seen a series of recent successes in problems from the fields of biology, chemistry, social science, physics, and many others. So far…

Basics of deep learning

La connoissance de certains principes supplée facilement à la connoissance de certains faits. (Claude Adrien Helvétius)

During my undergraduate studies, which I did in Electrical Engineering at the Technion in Israel, I was always appalled that such an important concept as convolution [1] just landed out of nowhere. This seemingly…

Michael Bronstein

Professor @imperialcollege, Head of Graph ML Research @Twitter, ML Lead @ProjectCETI. Researcher, teacher, entrepreneur

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