Clifford-VAE：将感知数据映射到符号推理的变分自编码器
Learning Holographic Reduced Representations with Clifford Variational Autoencoders
arXiv 上的新论文，Clifford-VAE 把图像数据编码成超维符号向量，做 VSA 基准测试比 Gaussian 和 Hyperspherical VAE 都强，搞神经符号方向的可以看看。
论文提出 Clifford-VAE，一种学习将数据投影到任意维度 Clifford 环面上的变分自编码器，用于向量符号代数（VSA）的数据嵌入。在 MNIST、FashionMNIST 和 CIFAR-10 上的实验显示，其半监督分类表现与 Gaussian VAE 和 Hyperspherical VAE 相当。在自绑定/解绑定、角色填充恢复和捆绑容量等 VSA 基准测试中，Clifford-VAE 超过了这两种基线方法。这为感知数据接入符号推理框架提供了一种原理化方案。
Learning Holographic Reduced Representations with Clifford Variational Autoencoders
Vector Symbolic Algebras project data structures into a hyperdimensional vector space through the application of their vector algebras to randomly generated atomic vector symbols and fractional power encodings of real-valued data. Embedding unstructured data remains an open question. We present \textit{Clifford-VAE}, a variational autoencoder that learns to project data onto a Clifford torus in arbitrary dimensions. Experiments using the MNIST, FashionMNIST, and CIFAR-10 datasets demonstrate that Clifford-VAE produces representations that are competitive with those produced by Gaussian and Hyperspherical VAEs for semi-supervised classification tasks while outperforming Gaussian and Hyperspherical counterparts in the VSA benchmark tests of self-binding and unbinding, role-filler recovery, and bundle capacity. Clifford-VAE provides a principled technique for grounding perceptual data into a symbolic reasoning framework, providing a new approach to a long-standing problem in the VSA literature.
- elvis09-23 15:35原文
