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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Modern deep neural networks require a significant amount of computing timeand power to train and deploy, which limits their usage on edge devices.Inspired by the iterative weight pruning in the Lottery Ticket Hypothesis, wepropose DropNet, an iterative pruning method which prunes nodes/filters toreduce network complexity. DropNet iteratively removes nodes/filters with thelowest average post-activation value across all training samples. Empirically,we show that DropNet is robust across diverse scenarios, including MLPs andCNNs using the MNIST, CIFAR-10 and Tiny ImageNet datasets. We show that up to90% of the nodes/filters can be removed without any significant loss ofaccuracy. The final pruned network performs well even with reinitialization ofthe weights and biases. DropNet also has similar accuracy to an oracle whichgreedily removes nodes/filters one at a time to minimise training loss,highlighting its effectiveness.

DropNet: Reducing Neural Network Complexity via Iterative Pruning