K-Inverse-RFM applies a label transformation to Recursive Feature Machines to close the performance gap with neural networks on data-corrupted mathematical tasks.
The k-inverse rfm framework
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K-Inverse-RFM: A Modified RFM that Bridges the Gap to Neural Networks for Data-Corrupted Mathematical Tasks
K-Inverse-RFM applies a label transformation to Recursive Feature Machines to close the performance gap with neural networks on data-corrupted mathematical tasks.