Berfin Inal

Connecting Neural Models Latent Geometries with Relative Geodesic Representations

Neural models learn representations of high-dimensional data on low-dimensional manifolds. Multiple factors, including stochasticities in the training process, model architectures, and additional inductive biases, may induce different representations, even when learning the same task on the same dat…