Sep 29, 2018 · In this paper, we provide such an analysis on the simple problem of ordinary least squares (OLS). Since precise dynamical properties of gradient ...

In this paper, we provide such an analysis on the simple problem of ordinary least squares. (OLS), where the precise dynamical properties of gradient descent ( ...

May 9, 2019 · Abstract. Despite its empirical success and recent theoret- ical progress, there generally lacks a quantita- tive analysis of the effect of ...

It is shown that unlike GD, gradient descent with BN (BNGD) converges for arbitrary learning rates for the weights, and the convergence remains linear under ...

Jun 9, 2019 · Batch normalization works well in practice, e.g. allows stable training with large learning rates, works well in high dimensions or ...

Despite its empirical success and recent theoretical progress, theregenerally lacks a quantitative analysis of the effect of batch normalization(BN) on the ...

In this work, we investigate the quantitative effect of applying batch normalization to simplified machine learning problems. In this case, we can prove ...

Block-normalized gradient method: An empirical study for training deep ... A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent.

Despite its empirical success and recent theoretical progress, there generally lacks a quantitative analysis of the effect of batch normalization (BN) on ...

This work establishes low training and test error guarantees of gradient descent. (GD) and stochastic gradient descent (SGD) on two-layer ReLU networks with.