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

Over-parameterization of deep neural networks (DNNs) has shown highprediction accuracy for many applications. Although effective, the large numberof parameters hinders its popularity on resource-limited devices and has anoutsize environmental impact. Sparse training (using a fixed number of nonzeroweights in each iteration) could significantly mitigate the training costs byreducing the model size. However, existing sparse training methods mainly useeither random-based or greedy-based drop-and-grow strategies, resulting inlocal minimal and low accuracy. In this work, we consider the dynamic sparsetraining as a sparse connectivity search problem and design an exploitation andexploration acquisition function to escape from local optima and saddle points.We further design an acquisition function and provide the theoreticalguarantees for the proposed method and clarify its convergence property.Experimental results show that sparse models (up to 98\% sparsity) obtained byour proposed method outperform the SOTA sparse training methods on a widevariety of deep learning tasks. On VGG-19 / CIFAR-100, ResNet-50 / CIFAR-10,ResNet-50 / CIFAR-100, our method has even higher accuracy than dense models.On ResNet-50 / ImageNet, the proposed method has up to 8.2\% accuracyimprovement compared to SOTA sparse training methods.

Dynamic Sparse Training via Balancing the Exploration-Exploitation Trade-off