Hyper-Representations: Learning from Populations of Neural Networks Publication date: 7 Oct 2024 Topic: Representation Learning Paper: https://arxiv.org/pdf/2410.05107v1.pdf GitHub: https://github.com/hsg-aiml/sane Description: This thesis addresses the challenge of understanding Neural Networks through the lens of their most fundamental component: the weights, which encapsulate the learned information and determine the model behavior. At the core of this thesis is a fundamental question: Can we learn general, task-agnostic representations from populations of Neural Network models? The key contribution of this thesis to answer that question are hyper-representations, a self-supervised method to learn representations of NN weights. Work in this thesis finds that trained NN models indeed occupy meaningful structures in the weight space, that can be learned and used.