Sgd when used as the training algorithm often seems to find empirical (near) global minima that also generalize well and have low test loss. Understanding the implicit bias of training algorithms is of crucial importance in order to explain the success of overparametrised neural networks. Hence while a general empirical risk minimization (erm).
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Stochastic gradient descent (sgd) plays a key role in training deep learning models, yet its ability to implicitly regularize and enhance generalization remains an open theoretical question. Training deep neural networks (dnns) with small batches using stochastic gradient descent (sgd) yields superior test performance compared to larger batches. There is a line of work approximating sgd with different.
19), there has been interest in the implicit bias of sgd.
Motivated by the empirical observation that sgd improves generalization (20; Abstract understanding the algorithmic bias of stochastic gradient descent (sgd) is one of the key challenges in modern machine learning and deep learning theory. Understanding the implicit bias of training algorithms is of crucial importance in order to explain the success of overparametrised neural networks. In this paper, we study the dynamics of stochastic.
In this paper, we study the dynamics of.
