提出迭代优化方法提升连续值编码的解码与去噪效果
Improved Cleanup and Decoding of Fractional Power Encodings
- 结合复合似然与最大似然，实现迭代优化解码
- 在多种噪声下均能有效恢复FHRR向量，优于现有方法
- 适用于需要高鲁棒性的神经符号计算场景
高维向量被用于脑神经信息表示，基于向量符号代数（VSAs）的框架中。尽管已有研究探索了计算过程中的噪声干扰下的解码与清理，但现有方法仍存在局限。针对连续值编码的清理效果不佳问题，本文提出一种迭代优化方法，用于解码与清理傅里叶全息约简表示（FHRR）的连续值编码向量。通过结合复合似然估计（CLE）与最大似然估计（MLE），确保收敛至全局最优。实验表明该方法在不同噪声条件下均能有效恢复原始向量，性能优于现有方法。
原文摘要 · Abstract (English)
High-dimensional vectors have been proposed as a neural method for representing information in the brain using Vector Symbolic Algebras (VSAs). While previous work has explored decoding and cleaning up these vectors under the noise that arises during computation, existing methods are limited. Cleanup methods are essential for robust computation within a VSA. However, cleanup methods for continuous-value encodings are not as effective. In this paper, we present an iterative optimization method to decode and clean up Fourier Holographic Reduced Representation (FHRR) vectors that are encoding continuous values. We combine composite likelihood estimation (CLE) and maximum likelihood estimation (MLE) to ensure convergence to the global optimum. We also demonstrate that this method can effectively decode FHRR vectors under different noise conditions, and show that it outperforms existing methods.
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