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Found 7 papers, 1 papers with code
Preliminary Report on Mantis Shrimp: a Multi-Survey Computer Vision Photometric Redshift Model
We reason about the behavior of the CNNs from the interpretability metrics, specifically framing the result in terms of physically-grounded knowledge of galaxy properties.
Efficient kernel surrogates for neural network-based regression
For the regression problem of smooth functions and logistic regression classification, we show that the CK performance is only marginally worse than that of the NTK and, in certain cases, is shown to be superior.
Foundation Model's Embedded Representations May Detect Distribution Shift
Sampling biases can cause distribution shifts between train and test datasets for supervised learning tasks, obscuring our ability to understand the generalization capacity of a model.
Exploring Learned Representations of Neural Networks with Principal Component Analysis
Understanding feature representation for deep neural networks (DNNs) remains an open question within the general field of explainable AI.
Faithful and Efficient Explanations for Neural Networks via Neural Tangent Kernel Surrogate Models
A second trend has been to utilize kernel functions in various explain-by-example or data attribution tasks.
TorchNTK: A Library for Calculation of Neural Tangent Kernels of PyTorch Models
We introduce torchNTK, a python library to calculate the empirical neural tangent kernel (NTK) of neural network models in the PyTorch framework.
GHOST: Using Only Host Galaxy Information to Accurately Associate and Distinguish Supernovae
We present GHOST, a database of 16, 175 spectroscopically classified supernovae and the properties of their host galaxies.
Dimensionality Reduction
Astrophysics of Galaxies
Cosmology and Nongalactic Astrophysics
Instrumentation and Methods for Astrophysics
