Testing the limits of bundling capacity (2)
How many records fit in a 2,048-dimensional hyperspace before unrelated records start to look alike? Our experiments suggest that the answer depends on how many comparisons we make.
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Explore everything we publish, from practical use cases and approachable HDC concepts to research perspectives and implementation.
How many records fit in a 2,048-dimensional hyperspace before unrelated records start to look alike? Our experiments suggest that the answer depends on how many comparisons we make.
How TrainableHD learns its encoder as well as its classes. We follow a mistake through training, work through an incident example, and examine the costs of deployment.
What happens as we pack more and more facts into a bundle? We study the limits of bundling capacity with a 50-item questionnaire.
A look back at how the HDC literature describes the terms "cleanup" and "crosstalk", and how we can view both terms through a modern lens using vector search in LanceDB.
An HDC experiment that separates stored examples from temporary class summaries, and compares rebuilding with caching.
Explore how HDC combines medical imaging and clinical language to classify retinal disease and support review of AI predictions.
The most similar past incident may not be the most useful. What if search could take today's equipment, conditions, and resources into account?
New to HDC? Start with these four introductions.
A gentle introduction to how hyperdimensional computing represents, combines, and retrieves information using high-dimensional hypervectors.
A closer look at the high-dimensional, distributed mathematical objects that give hyperdimensional computing its unusual geometry.
How Hyperdimensional Computing distributes information across an entire hypervector, helping representations degrade gradually under noise.
An introduction to the role of permutation in hyperdimensional computing and how it preserves structural information in ways that multiplicative binding cannot.
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