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Cut deep learning costs without sacrificing accuracy by swapping exact backpropagation for unbiased, randomized approximations of vector-Jacobian products.
Forget-set gradients, previously only used in uncertified heuristics, can now be harnessed for provably faster and more accurate machine unlearning.
