5 ms·
Don't know why you're downvoted since you're absolutely correct. They're just locality-sensitive hash functions.
by moralestapia 3y ago
Don't know why you're downvoted since you're absolutely correct.
They're just locality-sensitive hash functions.
- layer8 3y agoThose seem like two very different definitions.
- pizza 3y agolsh: similarity(x, y) < thresh => E[P(d(lsh(x), lsh(y)) < 1)] > 1-eps for some eps For a model that learns a good representation, and some suitable distance function to measure distances between embeddings (e.g. Euclidean) model: similarity(x, y) < thresh => E[P(d(emb(x), emb(y)) < r] > 1-eps for some cutoff distance r in the vector space
- charcircuit 3y agoThe difference in definitions is that my definition does not imply that the mapping maps similar objects to similar locations in space. Embeddings that do can be useful which is why you see people using machine learning to create them, but it is not a necessary property of an embedding.
- pizza 3y agoTrue, it’s not a necessity just based on the types of things, but in practice it’s the intended use
- corobo 3y agoI'd imagine it's because it's the title of the thing being linked to, not an actual question to be answered. Farting out a quick one line answer is a boring comment
- deleted 3y ago[deleted]
- quickthrower2 3y agoThere is "wikipedia article summary" kind of correct, but that doesn't mean resources that are aimed at teaching are not valuable too.
- rewmie 3y ago> Don't know why you're downvoted since you're absolutely correct. OP might be down voted because, even though the answer might be technically correct, it still completely fails to explain the concept (i.e., it's a useless explanation) and does so with a hint of arrogance and demeaning to readers. Yours too, by the way. It's like someone asks what is temperature and some smartass answers it's a scalar quantity that's mapped from a physical state into a semi-infinite continuous set. Great to know.