WaveletLM – wavelet-based, attention-free model with O(n log n) scaling
WaveletLM is a wavelet-based, attention-free architecture that replaces self-attention with learned lifting wavelet decomposition, a Fast Walsh-Hadamard Transform, per-scale gated spectral mixing with SwiGLU activation, an inverse FWHT, and wavelet reconstruction. Combined with expanded MLPs and sparse product-key memory, this yields a model with O(n log n) scaling in sequence length. With 23.8 PPL on WikiText-103, WaveletLM beats both GPT-2 Medium, which was trained on 80× more data, and Transformer-XL Standard, which uses recurrence to extend its effective context. It is undertrained and…
WaveletLM – wavelet-based, attention-free model with O(n log n) scaling launched on April 26, 2026 with 7 votes, #1,538 of 21,248 launches that month and more than 75% of that year's launches.
What it does
WaveletLM. Contribute to ramongougis/WaveletLM development by creating an account on GitHub.
In the maker’s words, at launch
WaveletLM is a wavelet-based, attention-free architecture that replaces self-attention with learned lifting wavelet decomposition, a Fast Walsh-Hadamard Transform, per-scale gated spectral mixing with SwiGLU activation, an inverse FWHT, and wavelet reconstruction. Combined with expanded MLPs and sparse product-key…
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