Summary of Hope For a Robust Parameterization Of Long-memory State Space Models, by Annan Yu et al.
HOPE for a Robust Parameterization of Long-memory State Space Models
by Annan Yu, Michael W. Mahoney, N. Benjamin Erichson
First submitted to arxiv on: 22 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to state-space models (SSMs) that utilizes linear, time-invariant (LTI) systems for learning long sequences. By viewing SSMs through the lens of Hankel operator theory, the authors develop a new parameterization scheme called HOPE, which improves initialization and training stability. This is achieved by nonuniformly sampling transfer functions of LTI systems, requiring fewer parameters than canonical SSMs. The proposed approach demonstrates improved performance on Long-Range Arena (LRA) tasks, outperforming HiPPO-initialized models such as S4 and S4D. Furthermore, the HOPE parameterization enables the SSM to maintain non-decaying memory within a fixed time window, empirically verified through a sequential CIFAR-10 task with padded noise. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand state-space models that use linear systems better. It shows how these models can learn long sequences by using a new way of setting up the model and training it. This makes the model more stable and easier to train, which is helpful for certain tasks like learning long sequences. |
