Trends in Graph Machine Learning with Michael Bronstein - #446
Published:Jan 11, 2021 22:35
•1 min read
•Practical AI
Analysis
This article from Practical AI summarizes a conversation with Michael Bronstein, a leading expert in Graph Machine Learning (Graph ML). The discussion covers Bronstein's perspective on the year in Machine Learning, including GPT-3 and Implicit Neural Representations. The primary focus, however, is on Graph ML, exploring its applications in fields like physics and bioinformatics, and highlighting key tools. The article concludes with Bronstein's predictions for 2021, specifically mentioning the application of Graph ML to molecule discovery and non-human communication translation. The interview format provides insights into the practical applications and future directions of Graph ML.
Key Takeaways
- •The article highlights the importance of Graph Machine Learning.
- •It discusses applications of Graph ML in various domains like physics and bioinformatics.
- •It mentions future applications of Graph ML, including molecule discovery and non-human communication translation.
Reference
“The article doesn't contain a direct quote, but summarizes the conversation.”