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

We present a novel methodology for repairing neural networks that use ReLUactivation functions. Unlike existing methods that rely on modifying theweights of a neural network which can induce a global change in the functionspace, our approach applies only a localized change in the function space whilestill guaranteeing the removal of the buggy behavior. By leveraging thepiecewise linear nature of ReLU networks, our approach can efficientlyconstruct a patch network tailored to the linear region where the buggy inputresides, which when combined with the original network, provably corrects thebehavior on the buggy input. Our method is both sound and complete -- therepaired network is guaranteed to fix the buggy input, and a patch isguaranteed to be found for any buggy input. Moreover, our approach preservesthe continuous piecewise linear nature of ReLU networks, automaticallygeneralizes the repair to all the points including other undetected buggyinputs inside the repair region, is minimal in terms of changes in the functionspace, and guarantees that outputs on inputs away from the repair region areunaltered. On several benchmarks, we show that our approach significantlyoutperforms existing methods in terms of locality and limiting negative sideeffects. Our code is available on GitHub:https://github.com/BU-DEPEND-Lab/REASSURE.

Sound and Complete Neural Network Repair with Minimality and Locality Guarantees