Where does meaning in a hypervector come from?
How projected and learned representations overcome the limitations of random codebooks (which lack semantic structure), bringing useful notions of similarity into hypervector space.
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Have a representation problem in mind?
If you're working with connected data, retrieval, agent memory or online learning, we'd love to hear what you're building and where the current representation is falling short.
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