Alphafold and Equivariance
Tim discusses DeepMind's groundbreaking Alphafold, showcasing its dominance in protein folding prediction. Justas and Fabian comment on DeepMind's approach, highlighting the importance of equivariance in neural networks for stable performance. Max Welling's involvement in the se three transformer for 3D point clouds and graphs is explored, emphasizing the significance of symmetries in data transformations.In this clip
From this podcast
Machine Learning Street Talk (MLST)
#036 - Max Welling: Quantum, Manifolds & Symmetries in ML
Related Questions