Summary of From Generalization Analysis to Optimization Designs For State Space Models, by Fusheng Liu et al.
From Generalization Analysis to Optimization Designs for State Space Models
by Fusheng Liu, Qianxiao Li
First submitted to arxiv on: 4 May 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 The abstract presents theoretical work on State Space Models (SSMs), an alternative to transformers in sequence modeling, with a focus on improving their training algorithms and generalization capabilities. The authors derive a data-dependent generalization bound for SSMs, showing how model parameters interact with temporal dependencies in training sequences. This bound is used to develop a scaling rule for initializing SSM models and a regularization method to enhance generalization performance. Numerical results demonstrate the effectiveness of these improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores ways to make State Space Models (SSMs) better at handling time series data. Currently, SSMs are being considered as an alternative to transformers in sequence modeling. The researchers investigate how well SSMs can generalize from one set of data to another and propose two main contributions: a new way to initialize the model based on the type of data it’s seeing, and a method to make the model less prone to overfitting. This work has practical implications for anyone working with time series data. |
Keywords
» Artificial intelligence » Generalization » Overfitting » Regularization » Time series
