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Found 6 papers, 2 papers with code
A principal odor map unifies diverse tasks in olfactory perception
Mapping molecular structure to odor perception is a key challenge in olfaction.
Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules
Based on these early results with graph neural networks for molecular properties, we hope machine learning can eventually do for olfaction what it has already done for vision and hearing.
AutoGraph: Imperative-style Coding with Graph-based Performance
In machine learning, imperative style libraries like Autograd and PyTorch are easy to write, but suffer from high interpretive overhead and are not easily deployable in production or mobile settings.
Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming
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 tools.
Tangent: Automatic Differentiation Using Source Code Transformation in Python
Automatic differentiation (AD) is an essential primitive for machine learning programming systems.
Composing graphical models with neural networks for structured representations and fast inference
We propose a general modeling and inference framework that composes probabilistic graphical models with deep learning methods and combines their respective strengths.
