3 ms·

Neural nets are just locality-sensitive hashing functions, after all.

• motohagiography 5y ago
  It's funny, but as a naive reader, I was reading through with the question of why you would use DCT whether it's significantly different from python's cosine_similarity, and why you would use that instead of say, pythagorean distance, edit distance, conditional entropy, and related methods. Is it a speed/accuracy trade off? Obviously I don't have depth on these as the answer would be trivial to someone who did, but I'm wondering if there a simple breakdown of what these and other information similarity methods optimize for.
• amitport 5y ago
  ? Any references?
  • wizzwizz4 5y ago
    When they convert a large number of inputs into a small number of outputs, they're acting like a hash function. (By this metric, lossy compression algorithms are all hash functions.)
  • rajnathani 5y ago
    Sparse neural networks can be optimized by implementing them using Locality Sensitive Hashing (2019 paper): https://arxiv.org/pdf/1903.03129.pdf https://arxiv.org/pdf/1903.03129.pdf Fun fact, last week one of the main authors of the above 2019 paper spun off a company for the above approach: https://techcrunch.com/2021/08/13/thirdai-raises-6m-to-democratize-ai-to-any-hardware/ https://techcrunch.com/2021/08/13/thirdai-raises-6m-to-democ...