Computer Science > Machine Learning
[Submitted on 27 Mar 2022 (v1), last revised 18 May 2022 (this version, v3)]
Title:Diagonal State Spaces are as Effective as Structured State Spaces
View PDFAbstract:Modeling long range dependencies in sequential data is a fundamental step towards attaining human-level performance in many modalities such as text, vision, audio and video. While attention-based models are a popular and effective choice in modeling short-range interactions, their performance on tasks requiring long range reasoning has been largely inadequate. In an exciting result, Gu et al. (ICLR 2022) proposed the $\textit{Structured State Space}$ (S4) architecture delivering large gains over state-of-the-art models on several long-range tasks across various modalities. The core proposition of S4 is the parameterization of state matrices via a diagonal plus low rank structure, allowing efficient computation. In this work, we show that one can match the performance of S4 even without the low rank correction and thus assuming the state matrices to be diagonal. Our $\textit{Diagonal State Space}$ (DSS) model matches the performance of S4 on Long Range Arena tasks, speech classification on Speech Commands dataset, while being conceptually simpler and straightforward to implement.
Submission history
From: Ankit Gupta [view email][v1] Sun, 27 Mar 2022 16:30:33 UTC (316 KB)
[v2] Tue, 17 May 2022 15:10:10 UTC (835 KB)
[v3] Wed, 18 May 2022 18:30:07 UTC (835 KB)
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