PyPI page
Author:
Hemant Sharma
Summary:
Domain-agnostic differentiable-inversion primitives for MIDAS: gradient fitting, scale-invariant losses, Laplace uncertainty, Fisher-information experiment design, mixture deconvolution, and amortised-inference surrogates.
Latest version:
0.2.0
Required dependencies:
numpy
|
torch
Optional dependencies:
pytest
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