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Abstract we consider a regularization problem whose objective function consists of a convex fidelity term and a regularization term determined by the ℓ 1 norm composed with a linear transform The estimation quality is generally sensitive to the values of multiple regularization parameters. Empirical results show that the regularization with the ℓ 1 norm can promote sparsity of a regularized solution.
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Using a combination of theoretical arguments and empirical results, we show that many common training heuristics such as parameter norm regularization, spectral norm regularization, flatness regularization, implicit gradient regularization, noise regularization and the choice of parameter initialization all act to control geometric complexity. Abstract recently, the type of compound regularizers has become a popular choice for signal reconstruction Abstract distribution shift poses a significant challenge for data analysis
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If a portion of the training samples does not match the data distribution in the real application, the predictive performance and generalization ability of the model will be reduced
This phenomenon is particularly evident in partial least squares (pls), a widely employed regression technique for addressing high. Besides, an l1 regularization term was added to obtain a sparse model and to avoid overfitting [6] Abstract modern regularization techniques are able to select the relevant variables and features in prediction problems where much more predictors than observations are available. Abstract the surrogate gradient (sg) method has shown significant promise in enhancing the performance of deep spiking neural networks (snns), but it also introduces vulnerabilities to adversarial attacks
Although spike coding strategies and neural dynamics parameters have been extensively studied for their impact on robustness, the critical role of gradient magnitude, which reflects the.
