WaveLiT combines wavelet tokenization, linear attention, and multiscale pyramids to produce parameter-efficient neural PDE solvers that match much larger models on TheWell benchmarks.
Wavelet-based image tokenizer for vision transformers.arXiv preprint arXiv:2405.18616, 2024
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A continuous-token model with shared Haar wavelet coefficients reports 39.92 dB audio, 29.37 dB image, and 23.93 dB video PSNR on three datasets and shows energy-based selection outperforms uniform selection by roughly 16 dB.
citing papers explorer
-
Small Models, Strong Priors: Architectural Inductive Bias for Parameter-Efficient Neural PDE Solvers
WaveLiT combines wavelet tokenization, linear attention, and multiscale pyramids to produce parameter-efficient neural PDE solvers that match much larger models on TheWell benchmarks.
-
Wavelet as Tokenizer: Preliminary Results on a Shared Wavelet Token Schema for Natural Signals
A continuous-token model with shared Haar wavelet coefficients reports 39.92 dB audio, 29.37 dB image, and 23.93 dB video PSNR on three datasets and shows energy-based selection outperforms uniform selection by roughly 16 dB.