We empirically demonstrate that both factorsĪre essential to fully explaining pruning's impact on generalization. These two factors improve training andĪdd regularization respectively. Influence: to affect or change how someone or something develops, behaves, or thinks. Impact: a powerful effect that something, especially something new, has on a situation or person. We find that pruning also leads toĪdditional regularization at other sparsities, reducing the accuracyĭegradation due to noisy examples over the dense model. Affect: to have an influence on someone or something, or to cause a change in someone or something. Instead, weįind that pruning leads to better training at specific sparsities, improving Generalization-improving effect of standard pruning algorithms. We show that size reduction cannot fully account for the ![]() Motivated by this contradiction, we re-examine pruning's effect on Size harms generalization, pruning to a range of sparsities nonetheless In other words, they wanted to bring about the effect of changemaybe by getting the government to change its policies or even step down. ![]() The protesters wanted to effect change in the corrupt government. Regime leads to a contradiction - while theory predicts that reducing model It usually shows up with nouns like change or solutions. ![]() Pruning models in this over-parameterized Over-parameterization characterize a new model size regime, in which larger Generalization improvement to model size reduction. ![]() Download a PDF of the paper titled Pruning's Effect on Generalization Through the Lens of Training and Regularization, by Tian Jin and 3 other authors Download PDF Abstract: Practitioners frequently observe that pruning improves model generalization.Ī long-standing hypothesis based on bias-variance trade-off attributes this
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