AI/ML+Physics Part 3: Designing an Architecture

In this discussion, we are focusing on classification and image segmentation using neural networks. The goal is to have the output of the neural network remain the same even if the input is rotated or translated. This concept is known as equivariance, where the function F of the machine learning model and the symmetry operation G should commute. This means that the output should maintain the same rotation or translation as the input.

Convolutional neural networks are known for promoting translation invariance, but recent research has shown how to incorporate general symmetry groups into other neural network architectures. By designing machine learning models with equivariant properties, we can reduce the amount of data needed for training and improve generalization.

Researchers like Max Welling and Tess Schmidt have made significant contributions to the field of equivariant machine learning models. In the upcoming sections, we will explore how to build these models, the use of specific loss functions and architectures, and their efficiency and generalization capabilities.

Stay tuned for discussions on loss functions, optimization, and various examples of machine learning architectures. Thank you for your attention.