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>Weiss figured out how to represent a complex image as a single hyperdimensional vector that contains information about all the objects in the image, including
by intalentive 4y ago
>Weiss figured out how to represent a complex image as a single hyperdimensional vector that contains information about all the objects in the image, including their properties, such as colors, positions and sizes.
How is this different from an N-dimensional embedding produced by, say, a convolutional neural network?
- esafak 4y ago> The team then trained a neural network to examine an image and generate a bipolar hypervector — an element can be +1 or −1 — that’s as close as possible to some superposition of hypervectors in the dictionary... It seems the former uses binary embeddings, which in ML is used more frequently for similarity search. They also seem to care about orthogonality. Beyond that we'd have to read the paper to know how the embeddings are derived. If you're motivated, here they are: http://www.rctn.org/bruno/papers/ http://www.rctn.org/bruno/papers/
- HybridCurve 4y agoI am reasonably certain this is the paper the article is discussing: https://redwood.berkeley.edu/wp-content/uploads/2018/01/rahimi2017high.pdf https://redwood.berkeley.edu/wp-content/uploads/2018/01/rahi...