Farhad Pashakhanloo

LLMs unlock a scalable path to predicting the perceptual geometry of complex odor mixtures, revealing learned molecular embeddings that align with human perception 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.