Farhad Pashakhanloo

LLMs unlock a scalable pathway to predict the perceptual geometry of complex natural odors, revealing learned molecular embeddings that capture human-like olfactory similarity even for mixtures with no shared components.

Undercomplete linear autoencoders exhibit a directional bias in scale symmetry breaking, with SGD consistently favoring larger decoder weights on the PCA manifold.