English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We consider networks, trained via stochastic gradient descent to minimize$\ell_2$ loss, with the training labels perturbed by independent noise at eachiteration. We characterize the behavior of the training dynamics near anyparameter vector that achieves zero training error, in terms of an implicitregularization term corresponding to the sum over the data points, of thesquared $\ell_2$ norm of the gradient of the model with respect to theparameter vector, evaluated at each data point. This holds for networks of anyconnectivity, width, depth, and choice of activation function. We interpretthis implicit regularization term for three simple settings: matrix sensing,two layer ReLU networks trained on one-dimensional data, and two layer networkswith sigmoid activations trained on a single datapoint. For these settings, weshow why this new and general implicit regularization effect drives thenetworks towards "simple" models.

Implicit regularization for deep neural networks driven by an Ornstein-Uhlenbeck like process