Today we’re back with the final episode of AI Rewind joined by Michael Bronstein, a professor at Imperial College London and the Head of Graph Machine Learning at Twitter.
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In our conversation with Michael, we touch on his thoughts about the year in Machine Learning overall, including GPT-3 and Implicit Neural Representations, but spend a major chunk of time on the sub-field of Graph Machine Learning.
We talk through the application of Graph ML across domains like physics and bioinformatics, and the tools to look out for. Finally, we discuss what Michael thinks is in store for 2021, including graph ml applied to molecule discovery and non-human communication translation.
To follow along with the 2020 AI Rewind Series, head over to the series page.
Connect with Michael!
- Graph ML Research at Twitter with Michael Bronstein
- Neural Execution of Graph Algorithms
- On the Bottleneck of Graph Neural Networks and its Practical Implications
- A Deep Learning Approach to Antibiotic Discovery
- AlphaFold: a solution to a 50-year-old grand challenge in biology
- Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
- Open Graph Benchmark
- Relation Therapeutics
- Ariel AI
- HyperFoods: Machine intelligent mapping of cancer-beating molecules in foods
- Project CETI
- Check out our TWIML Presents: series page!
- Register for the TWIML Newsletter
- Check out the official TWIMLcon:AI Platform video packages here!
- Download our latest eBook, The Definitive Guide to AI Platforms!
“More On That Later” by Lee Rosevere licensed under CC By 4.0