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

Inspired by recent successes with parallel optimization techniques forsolving Boolean satisfiability, we investigate a set of strategies andheuristics that aim to leverage parallel computing to improve the scalabilityof neural network verification. We introduce an algorithm based on partitioningthe verification problem in an iterative manner and explore two partitioningstrategies, that work by partitioning the input space or by case splitting onthe phases of the neuron activations, respectively. We also introduce a highlyparallelizable pre-processing algorithm that uses the neuron activation phasesto simplify the neural network verification problems. An extensive experimentalevaluation shows the benefit of these techniques on both existing benchmarksand new benchmarks from the aviation domain. A preliminary experiment withultra-scaling our algorithm using a large distributed cloud-based platform alsoshows promising results.

Parallelization Techniques for Verifying Neural Networks