<ul><li>Neural networks can be viewed as dynamical systems operating on a lower-dimensional latent space, according to a new study.</li><li>Autoencoder models inherently establish a latent vector field on the manifold without extra training, with training processes influencing the emergence of attractor points.</li><li>The proposed vector field representation offers insights into neural network properties, facilitating the analysis of generalization, memorization, and prior knowledge extraction from network parameters.</li><li>The study demonstrates the efficacy of this approach on vision foundation models, emphasizing its practical utility in real-world contexts.</li></ul>

Navigating the Latent Space Dynamics of Neural Models

Discover more