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#(please summarize and share possible use cases)

• hiddencost 3y ago
  Number go up. More seriously, it looks like a potential reduction in the cost to train a neural network. You can think of every meaningful step forward in deep learning as a reduction in the cost of training, or an improvement in the ability of the signal to propagate. https://arxiv.org/abs/2001.08361 https://arxiv.org/abs/2001.08361
  • jxmorris12 3y ago
    No neither of these explanations is right. The paper talks about a specific type of model called an embedding model that produces vectors for datapoints that are useful for downstream tasks. Normally you have to choose a single dimensionality for the vectors and store them all in a database that’s proportional to the size of those vectors. The method described is for a model that has multiple “options” for the lengths of its embeddings. You can use smaller vectors to save space but they don’t work quite as well. Paper analyzes this more in-depth.