A framework embeds statistical descriptors of tabular datasets via sentence transformers and applies penalized CCA to enable similarity retrieval and sparse interpretable alignment across heterogeneous numeric data.
URL https://www.jstor.org/stable/2984875
2 Pith papers cite this work, alongside 666 external citations. Polarity classification is still indexing.
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Nash integrates neural networks into variational empirical Bayes to learn per-covariate penalties for sparse high-dimensional regression, claiming major speedups and better accuracy on real data.
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Statistical Embeddings for Similarity, Retrieval, and Interpretable Alignment of Numeric Tabular Datasets
A framework embeds statistical descriptors of tabular datasets via sentence transformers and applies penalized CCA to enable similarity retrieval and sparse interpretable alignment across heterogeneous numeric data.
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Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression
Nash integrates neural networks into variational empirical Bayes to learn per-covariate penalties for sparse high-dimensional regression, claiming major speedups and better accuracy on real data.