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.
On the difficulty of training recurrent neural networks
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Simple training code produces complex neural networks, suggesting that brain learning rules may be easier to understand than mature brain properties and that neuroscience should shift focus accordingly.
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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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What does it mean to understand a neural network?
Simple training code produces complex neural networks, suggesting that brain learning rules may be easier to understand than mature brain properties and that neuroscience should shift focus accordingly.