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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Understanding the fundamental principles behind the success of deep neuralnetworks is one of the most important open questions in the current literature.To this end, we study the training problem of deep neural networks andintroduce an analytic approach to unveil hidden convexity in the optimizationlandscape. We consider a deep parallel ReLU network architecture, which alsoincludes standard deep networks and ResNets as its special cases. We then showthat pathwise regularized training problems can be represented as an exactconvex optimization problem. We further prove that the equivalent convexproblem is regularized via a group sparsity inducing norm. Thus, a pathregularized parallel ReLU network can be viewed as a parsimonious convex modelin high dimensions. More importantly, since the original training problem maynot be trainable in polynomial-time, we propose an approximate algorithm with afully polynomial-time complexity in all data dimensions. Then, we prove strongglobal optimality guarantees for this algorithm. We also provide experimentscorroborating our theory.

Path Regularization: A Convexity and Sparsity Inducing Regularization for Parallel ReLU Networks