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

Deep learning has emerged as an effective approach for creating modernsoftware systems, with neural networks often surpassing hand-crafted systems.Unfortunately, neural networks are known to suffer from various safety andsecurity issues. Formal verification is a promising avenue for tackling thisdifficulty, by formally certifying that networks are correct. We propose anSMT-based technique for verifying Binarized Neural Networks - a popular kind ofneural network, where some weights have been binarized in order to render theneural network more memory and energy efficient, and quicker to evaluate. Onenovelty of our technique is that it allows the verification of neural networksthat include both binarized and non-binarized components. Neural networkverification is computationally very difficult, and so we propose here variousoptimizations, integrated into our SMT procedure as deduction steps, as well asan approach for parallelizing verification queries. We implement our techniqueas an extension to the Marabou framework, and use it to evaluate the approachon popular binarized neural network architectures.

An SMT-Based Approach for Verifying Binarized Neural Networks