Testing the limits of bundling capacity (1)
What happens as we pack more and more facts into a bundle? We study the limits of bundling capacity with a 50-item questionnaire.
Research
Research perspectives
R&D is a strong focus of our work at HDC Labs. We examine research across HDC, graph intelligence and deep learning, develop our own perspective, and explore how those ideas translate into building practical systems.
Research
Showing 1–5 of 9 posts
What can we recover from a bundled hypervector?
Five MAP experiments explore how bundled hypervectors support semantic retrieval, explainable scores, query preferences, and removal of known facts.
A category is something a system does
What a new neuroscience framework proposes about context, compression, and action, and how HDC could turn those ideas into a computational architecture.
Does HDC fit the physics of light? Reviewing PhotoHDC
A critical review of PhotoHDC's modeled electro-photonic accelerator, what its evidence supports, and how OLIX and recent optical HDC work sharpen the deployment question.
Do HRR and MAP preserve the same similarity structure?
A reproducible experiment that tests whether HRR and MAP preserve the same similarity structure in a shared encoder.
Contact
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.
Get in touch
We usually reply within two business days.