Data Augmentations vs. Architectures

Michael discusses the balance between incorporating prior knowledge into data augmentations versus architecture design in machine learning. He highlights the importance of achieving geometric stability rather than perfect invariance for effective model performance.

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From this podcast
Machine Learning Street Talk (MLST)
#60 Geometric Deep Learning Blueprint (Special Edition)
Related Questions
- Explain equivariant neural networks
- What is the difference between equivariant and invariant layers in neural networks as discussed in the episode #71 - ZAK JOST (Graph Neural Networks + Geometric DL) \[UNPLUGGED] and the clip Geometric Deep Learning Blueprint?