Summary of Crossmpt: Cross-attention Message-passing Transformer For Error Correcting Codes, by Seong-joon Park et al.
CrossMPT: Cross-attention Message-Passing Transformer for Error Correcting Codes
by Seong-Joon Park, Hee-Youl Kwak, Sang-Hyo Kim, Yongjune Kim, Jong-Seon No
First submitted to arxiv on: 2 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT)
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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 proposed Cross-attention Message-Passing Transformer (CrossMPT) is a novel ECC decoder that leverages neural networks to achieve state-of-the-art decoding performance. By updating magnitude and syndrome vectors separately using masked cross-attention blocks, CrossMPT outperforms existing transformer-based decoders while reducing memory usage, complexity, inference time, and training time. The architecture shares key operational principles with conventional message-passing decoders and is designed to explicitly capture irrelevant relationships between input vectors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CrossMPT is a new way to decode error-correcting codes using neural networks. It’s better than other methods at decoding these types of codes while also being more efficient. The paper shows that CrossMPT works well for different code types and is faster and uses less memory than existing methods. |
Keywords
» Artificial intelligence » Cross attention » Decoder » Inference » Transformer
