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arXiv · 2502.12381

Linear Diffusion Networks

Jacob Fein-Ashley
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Abstract

We present Linear Diffusion Networks (LDNs), a novel architecture that reinterprets sequential data processing as a unified diffusion process. Our model integrates adaptive diffusion modules with localized nonlinear updates and a diffusion-inspired attention mechanism. This design enables efficient global information propagation while preserving fine-grained temporal details. LDN overcomes the limitations of conventional recurrent and transformer models by allowing full parallelization across time steps and supporting robust multi-scale temporal representations. Experiments on benchmark sequence modeling tasks demonstrate that LDN delivers competitive performance across ImageNet and LRA tasks.

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BibTeXRIS

Jacob Fein-Ashley. 2025-02-17. Linear Diffusion Networks. https://arxiv.org/abs/2502.12381

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