# Wearing a MASK: Compressed Representations of Variable-Length Sequences Using Recurrent Neural Tangent Kernels

arXiv: https://arxiv.org/abs/2010.13975 · HTML: https://mlsanity.com/paper/2010.13975/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [SaFARi: State-Space Models for Frame-Agnostic Representation](https://mlsanity.com/paper/2505.08977/) (2025)
- [WaLRUS: Wavelets for Long-range Representation Using SSMs](https://mlsanity.com/paper/2505.12161/) (2025)
- [W4S4: WaLRUS Meets S4 for Long-Range Sequence Modeling](https://mlsanity.com/paper/2506.07920/) (2025)

## Abstract (arXiv)

High dimensionality poses many challenges to the use of data, from visualization and interpretation, to prediction and storage for historical preservation. Techniques abound to reduce the dimensionality of fixed-length sequences, yet these methods rarely generalize to variable-length sequences. To address this gap, we extend existing methods that rely on the use of kernels to variable-length sequences via use of the Recurrent Neural Tangent Kernel (RNTK). Since a deep neural network with ReLu activation is a Max-Affine Spline Operator (MASO), we dub our approach Max-Affine Spline Kernel (MASK). We demonstrate how MASK can be used to extend principal components analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) and apply these new algorithms to separate synthetic time series data sampled from second-order differential equations.