Summary of Autocorrelation Matters: Understanding the Role Of Initialization Schemes For State Space Models, by Fusheng Liu et al.
Autocorrelation Matters: Understanding the Role of Initialization Schemes for State Space Models
by Fusheng Liu, Qianxiao Li
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper investigates the role of initialization schemes for state space models (SSMs) by considering the temporal structures of input sequences. The current HiPPO framework for initializing SSM parameters does not account for these effects, which can impact optimization results. To address this gap, the authors rigorously characterize the dependency of the SSM timescale on sequence length based on autocorrelation, and show that a proper timescale mitigates the curse of memory while maintaining stability at initialization. Additionally, they uncover an approximation-estimation tradeoff when training SSMs with specific target functions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks into how we can make state space models work better by thinking about the patterns in the data. Right now, most people use a method called HiPPO to start their models off right, but this method doesn’t take into account some important features of the data. The researchers did some math to figure out what these features do and how they can help or hurt our models. They found that if we choose the right “timescale” for our model, it will be more stable and work better even with really long sequences of data. |
Keywords
» Artificial intelligence » Optimization
