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Found 4 papers, 2 papers with code
Measuring chemical likeness of stars with RSCA
RSCA finds a mapping from stellar spectra to a representation that optimizes recovery of known open clusters.
Disentangled Representation Learning for Astronomical Chemical Tagging
To remove known non-chemical factors of variation, we develop and implement a neural network architecture that learns a disentangled spectral representation.
Human-interpretable model explainability on high-dimensional data
The importance of explainability in machine learning continues to grow, as both neural-network architectures and the data they model become increasingly complex.
Shapley explainability on the data manifold
Explainability in AI is crucial for model development, compliance with regulation, and providing operational nuance to predictions.
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