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Michael Bronstein

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Michael Bronstein
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Towards Data Science

The Road to Biology 2.0 Will Pass Through Black-Box Data

Future bio-AI breakthroughs will arise from novel high-throughput low-cost AI-specific “black-box” data modalities.

Mar 18

The Road to Biology 2.0 Will Pass Through Black-Box Data

Mar 18

Michael Bronstein
in
Towards Data Science

Co-operative Graph Neural Networks

A new message-passing paradigm where every node can choose to either ‘listen’, ‘broadcast’, ‘listen & broadcast’ or ‘isolate’.

Dec 6, 2023

Co-operative Graph Neural Networks

Dec 6, 2023

Michael Bronstein
in
Towards Data Science

Topological Generalisation with Advective Diffusion Transformers

A new diffusion-based continuous GNN model offers better generalisation capabilities

Oct 19, 2023

Topological Generalisation with Advective Diffusion Transformers

Oct 19, 2023

Michael Bronstein
in
Towards Data Science

Dynamically rewired delayed message passing GNNs

Dynamic rewiring and delayed message passing mechanisms offer a tradeoff between standard MPNNs and graph Transformers

Jun 19, 2023

Dynamically rewired delayed message passing GNNs

Jun 19, 2023

Michael Bronstein
in
Towards Data Science

Direction Improves Graph Learning

How a wise use of direction when doing message passing on heterophilic graphs can result in very significant gains.

Jun 8, 2023

Direction Improves Graph Learning

Jun 8, 2023

Michael Bronstein
in
Towards Data Science

Hyperbolic Deep Reinforcement Learning

Many RL problems have hierarchical tree-like nature. Hyperbolic geometry offers a powerful prior for such problems.

Apr 30, 2023

Hyperbolic Deep Reinforcement Learning

Apr 30, 2023

Michael Bronstein
in
Towards Data Science

Learning Network Games

How to learn the network underlying the interactions of players in social applications, economics, and beyond.

Apr 20, 2023

Learning Network Games

Apr 20, 2023

Michael Bronstein
in
Towards Data Science

Graph Neural Networks as gradient flows

GNNs derived as gradient flows minimising a learnable energy that describes attractive and repulsive forces between graph nodes.

Oct 14, 2022

Graph Neural Networks as gradient flows

Oct 14, 2022

Michael Bronstein
in
Towards Data Science

Towards Geometric Deep Learning IV: Chemical Precursors of GNNs

In the last post in the “Towards Geometric Deep Learning” series, we look at early prototypes of GNNs in the field of chemistry.

Jul 25, 2022

Towards Geometric Deep Learning IV: Chemical Precursors of GNNs

Jul 25, 2022

Michael Bronstein
in
Towards Data Science

Towards Geometric Deep Learning III: First Geometric Architectures

In the third post of our series “Towards Geometric Deep Learning” we look at the first “geometric” architectures: Neocognitron and CNNs

Jul 18, 2022

Towards Geometric Deep Learning III: First Geometric Architectures

Jul 18, 2022

Michael Bronstein

Michael Bronstein

11.6K Followers

DeepMind Professor of AI @Oxford. Serial startupper. ML for graphs, biochemistry, drug design, and animal communication.

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