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  • Decoupled Weight Decay Regularization - OpenReview
    L$_2$ regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we demonstrate this is \emph {not} the case for adaptive gradient algorithms, such as Adam While common implementations of these algorithms employ L$_2$ regularization (often calling it ``weight decay'' in what may be misleading due to the
  • D WEIGHT DECAY REGULARIZATION - OpenReview
    L2 regularization and weight decay are not identical Contrary to a belief which seems popular among some practitioners, the two techniques are not equivalent For SGD, they can be made equivalent by a reparameterization of the weight decay factor based on the learning rate; this is not the case for Adam In particular, when combined with adaptive gradients, L2 regularization leads to weights
  • F WEIGHT DECAY REGULARIZATION IN A - OpenReview
    ABSTRACT We note that common implementations of adaptive gradient algorithms, such as Adam, limit the potential benefit of weight decay regularization, because the weights do not decay multiplicatively (as would be expected for standard weight decay) but by an additive constant factor We propose a simple way to resolve this issue by decoupling weight decay and the optimization steps taken w r
  • DECOUPLEDORTHOGONALDYNAMICS REGULARIZATION FORDEEPNETWORKOPTIMIZERS
    Is the standard weight decay in AdamW truly optimal? Although AdamW decouples weight decay from adaptive gradient scaling, a fundamental conflict remains: the Radial Tug-of-War In deep learning, gradients tend to increase pa-rameter norms to expand effective capacity while steering directions to learn fea-tures, whereas weight decay indiscriminately suppresses norm growth This push– pull
  • STABLE WEIGHT DECAY REGULARIZATION - OpenReview
    Second, decoupled weight decay is highly unstable for all adaptive gradient methods We further propose the Stable Weight Decay (SWD) method to fix the unstable weight decay problem from a dynamical perspective The proposed SWD method makes significant improvements over L2 regularization and decoupled weight decay in our experiments
  • Cautious Weight Decay - OpenReview
    We introduce Cautious Weight Decay (CWD), a one-line, optimizer-agnostic modification that applies weight decay only to parameter coordinates whose signs align with the optimizer update Unlike standard decoupled decay, which implicitly optimizes a regularized or constrained objective, CWD preserves the original loss and admits a bilevel interpretation: it induces sliding-mode behavior upon
  • Adam-family Methods with Decoupled Weight Decay in DeepLearning
    Can we design Adam-family methods with decoupled weight decay that have convergence guarantees with non-diminishing stepsizes, in the context of training nonsmooth neural networks?
  • Fixing Weight Decay Regularization in Adam - OpenReview
    Abstract: We note that common implementations of adaptive gradient algorithms, such as Adam, limit the potential benefit of weight decay regularization, because the weights do not decay multiplicatively (as would be expected for standard weight decay) but by an additive constant factor We propose a simple way to resolve this issue by decoupling weight decay and the optimization steps taken w
  • U SCHEDULING WEIGHT DECAY - OpenReview
    Weight decay is a popular and even necessary regularization technique for training deep neural networks that generalize well (Krogh and Hertz, 1992) People commonly use L2 regularization as “weight decay” for training of deep neural networks and interpret it as a Gaussian prior over the model weights (David, 1992; Graves, 2011) This is true for vanilla Stochastic Gradient Descent (SGD
  • Decoupled Orthogonal Dynamics: Regularization for Deep Network . . .
    Is the standard weight decay in AdamW truly optimal? Although AdamW decouples weight decay from adaptive gradient scaling, a fundamental conflict remains: the Radial Tug-of-War In deep learning





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