SILO: Shaping and Sampling from Learned Embeddings
Abstract Neural networks and other architectures progressively reshape the geometry of data space from data’s raw form to one that better suits high-level tasks. This geometric perspective suggests that we can manipulate the shape of these embeddings to improve interpretability or performance on certain tasks, and that we can devise …