High-Performance Implementation of Spectral Learning of Latent-Variable PCFGs (Cohen et al., 2013)
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Updated
Apr 11, 2021 - Python
High-Performance Implementation of Spectral Learning of Latent-Variable PCFGs (Cohen et al., 2013)
A Python package implementing Rectified Spectral Units (ReSUs), a biologically inspired neural building block for backprop-free training using spectral decomposition and Canonical Correlation Analysis (CCA).
Code for "Automata in Space: Formal Language Theory meets Neural Computation"
A from-scratch implementation of Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) using NumPy. Explores dimensionality reduction, linear algebra, and clustering on the Wine Quality dataset.
Simple spectral learning for weighted automata
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