S4 is an efficient state space sequence model that captures long-range dependencies via structured parameterization of the SSM, achieving state-of-the-art results on the Long Range Arena and other benchmarks while being faster than Transformers for generation.
arXiv preprint arXiv:2206.12037 , title =
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Medusa augments LLMs with multiple decoding heads and tree-based attention to predict and verify several tokens in parallel, yielding 2.2-3.6x inference speedup via two fine-tuning regimes.
S4 models exhibit stable time-continuity unlike sensitive S6 models, with task continuity predicting performance and enabling temporal subsampling for better efficiency.
A new regularizer transfers frequency awareness from state-space models into image tokenizers, yielding more compact latents that improve diffusion-model generation quality with little reconstruction penalty.
citing papers explorer
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Efficiently Modeling Long Sequences with Structured State Spaces
S4 is an efficient state space sequence model that captures long-range dependencies via structured parameterization of the SSM, achieving state-of-the-art results on the Long Range Arena and other benchmarks while being faster than Transformers for generation.
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Continuity Laws for Sequential Models
S4 models exhibit stable time-continuity unlike sensitive S6 models, with task continuity predicting performance and enabling temporal subsampling for better efficiency.
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Structured State-Space Regularization for Compact and Generation-Friendly Image Tokenization
A new regularizer transfers frequency awareness from state-space models into image tokenizers, yielding more compact latents that improve diffusion-model generation quality with little reconstruction penalty.