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What is Breaking Power Law Scaling with Pruning about?

Widely observed neural scaling laws, in which error falls off as a power of the training set size, model size, or both, have driven substantial performance im- provements in deep learning. However, these improvements through scaling alone require considerable costs in compute and energy. Here we focus on the scaling of error with dataset size and show how in theory we can break beyond power law scaling and potentially even reduce it to exponential scaling instead if we have access to a high-qual

Author

Soon

Language

EN