97 citations · 98 across the 2 of their papers we have counts for
3 papers
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases
Gary Tom, Cher Tian Ser, Ella M. Rajaonson +4
Olfaction -- how molecules are perceived as odors to humans -- remains poorly understood. Recently, the principal odor map (POM) was introduced to digitize the olfactory properties…
Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules
Benjamin Sanchez-Lengeling, Jennifer N. Wei, Brian K. Lee +3
Predicting the relationship between a molecule's structure and its odor remains a difficult, decades-old task. This problem, termed quantitative structure-odor relationship (QSOR)…
AutoGraph: Imperative-style Coding with Graph-based Performance
Dan Moldovan, James M Decker, Fei Wang +6
There is a perceived trade-off between machine learning code that is easy to write, and machine learning code that is scalable or fast to execute. In machine learning, imperative s…