Erlangen AI Hub Seminar: A Topological Lens on Neural Networks, Qiquan Wang

Neural networks exhibit rich geometric and topological structure across their weights, activations, and representation spaces. This talk surveys a number of methods for probing these structures, providing complementary perspectives on how representations evolve across depth, encode structure, and respond to perturbations. I will highlight a recent piece of work studying the effect of adversarial influence on large language model latent spaces using persistent homology.

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