Approximate Symmetries in Deep Learning

Tim and Taco discuss the importance of approximate symmetries in deep learning models, highlighting the need for stability and robustness rather than strict invariance. Petar explains how skip connections can allow models to leverage symmetries while maintaining flexibility, especially in scenarios where classical algorithms struggle to capture complex real-world data.