Plenary Talk: Geometry and Latent Signal Representations in Machine Learning

The advent of deep neural networks has brought significant advancements in the development and deployment of novel AI technologies. Recent large-scale neural network architectures have demonstrated remarkable performance in object classification, scene understanding, language processing, and multimodal generative AI. How can we understand how the representations of input signals are transformed within deep neural networks? I will explain how statistical insights can be gained by analyzing the high-dimensional geometrical structure of these representations as they are reformatted by neural network hierarchies of basic perceptron units.