Summary
This post introduces tridiagonal eigenvalue models in PyTorch, which promise cheaper training and inference compared to dense spectral models. The broader research aims to understand the capabilities of matrix eigenvalue non-linearity as a "neuron," seeking a useful middle ground between interpretable linear models and expressive but opaque neural networks. This work explores an alternative architectural approach in the current AI landscape, focusing on efficiency and interpretability.
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[r/ML] [D] MYTHOS-INVERSION STRUCTURAL AUDIT
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