Summary of Generalized Holographic Reduced Representations, by Calvin Yeung et al.
Generalized Holographic Reduced Representations
by Calvin Yeung, Zhuowen Zou, Mohsen Imani
First submitted to arxiv on: 15 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Symbolic Computation (cs.SC)
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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 introduces Hyperdimensional Computing (HDC), a brain-inspired approach that efficiently learns representations while addressing the massive energy, compute, and data costs associated with deep learning. HDC bridges connectionist and symbolic AI approaches by allowing explicit specification of representational structure, offering flexibility and robustness. However, HDC’s simplicity hinders its ability to encode complex compositional structures. To address this, the authors propose Generalized Holographic Reduced Representations (GHRR), an extension of Fourier Holographic Reduced Representations (FHRR). GHRR introduces a non-commutative binding operation, enhancing decoding accuracy for compositional structures and memorization capacity compared to FHRR. The authors prove GHRR’s theoretical properties, explore its kernel and binding characteristics, and demonstrate its effectiveness through empirical experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at an alternative way of doing artificial intelligence called Hyperdimensional Computing (HDC). HDC is good because it uses less energy, computer power, and data compared to other AI methods. However, it’s not great at handling complex patterns in data. To fix this, the authors created a new approach called Generalized Holographic Reduced Representations (GHRR). GHRR makes it easier for computers to learn from complex patterns and remember things better. The paper shows how well GHRR works by testing it on some examples. |
Keywords
» Artificial intelligence » Deep learning
