Summary of Model Compression Method For S4 with Diagonal State Space Layers Using Balanced Truncation, by Haruka Ezoe and Kazuhiro Sato
Model Compression Method for S4 with Diagonal State Space Layers using Balanced Truncation
by Haruka Ezoe, Kazuhiro Sato
First submitted to arxiv on: 25 Feb 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 proposes a novel model compression method for implementing deep learning models on edge devices, specifically tailored for processing long-sequence data. The authors introduce a technique called balanced truncation, commonly used in control theory, to compress Diagonal State Space (DSS) layers in pre-trained Structured State Space Sequence (S4) models. They then use the reduced model parameters as initial parameters during the main training process, achieving improved accuracy even with fewer parameters compared to conventionally trained models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to make deep learning models smaller and faster, so they can run on devices like smartphones or smart home devices. The researchers used a technique called “balanced truncation” that helps reduce the size of these models without losing their ability to learn from large amounts of data. They tested this method with different types of models and found that it works well, even when using fewer model parameters. This could lead to faster and more efficient AI systems in the future. |
Keywords
* Artificial intelligence * Deep learning * Model compression
