Summary of Efficientstate Space Model Viafast Tensor Convolutionand Block Diagonalization, by Tongyi Liang and Han-xiong Li
EfficientState Space Model viaFast Tensor Convolutionand Block Diagonalization
by Tongyi Liang, Han-Xiong Li
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes a novel state space model called efficient SSM (eSSM) to tackle the challenge of modeling long sequences while balancing performance and computational efficiency. Building on the convolutional representation of multi-input multiple-output SSM, eSSM leverages diagonalization and block diagonalization strategies to reduce the number of parameters and improve flexibility. Experimental results demonstrate that eSSM matches the performance of state-of-the-art models like S4 and outperforms Transformers and LSTM on multiple databases. Furthermore, eSSM exhibits significantly improved model efficiency, with parameter counts 12.89% and 13.24% lower than LSTM and Mamba, respectively, and training speeds 3.94 times faster than LSTM and 1.35 times faster than Mamba. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to model long sequences of data efficiently. Currently, existing models struggle to balance how well they perform and how much computer power they use when dealing with long sequences. The researchers propose a new state space model called eSSM that can do this better. They achieve this by using special techniques like diagonalization and block diagonalization to reduce the number of calculations needed. The results show that eSSM performs just as well as other top models, but uses less computer power. |
Keywords
* Artificial intelligence * Lstm
