216 citations · 361 across the 5 of their papers we have counts for
6 papers
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)…
Avoiding a Tragedy of the Commons in the Peer Review Process
D Sculley, Jasper Snoek, Alex Wiltschko
Peer review is the foundation of scientific publication, and the task of reviewing has long been seen as a cornerstone of professional service. However, the massive growth in the f…
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…
Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming
Bart van Merriënboer, Dan Moldovan, Alexander B Wiltschko
The need to efficiently calculate first- and higher-order derivatives of increasingly complex models expressed in Python has stressed or exceeded the capabilities of available tool…
Tangent: Automatic Differentiation Using Source Code Transformation in Python
Bart van Merriënboer, Alexander B. Wiltschko, Dan Moldovan
Automatic differentiation (AD) is an essential primitive for machine learning programming systems. Tangent is a new library that performs AD using source code transformation (SCT)…
Composing graphical models with neural networks for structured representations and fast inference
Matthew J. Johnson, David Duvenaud, Alexander B. Wiltschko +2
We propose a general modeling and inference framework that composes probabilistic graphical models with deep learning methods and combines their respective strengths. Our model fam…