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 FACADE, a novel probabilistic and geometric framework designed forunsupervised mechanistic anomaly detection in deep neural networks. Its primarygoal is advancing the understanding and mitigation of adversarial attacks.FACADE aims to generate probabilistic distributions over circuits, whichprovide critical insights to their contribution to changes in the manifoldproperties of pseudo-classes, or high-dimensional modes in activation space,yielding a powerful tool for uncovering and combating adversarial attacks. Ourapproach seeks to improve model robustness, enhance scalable model oversight,and demonstrates promising applications in real-world deployment settings.

FACADE: A Framework for Adversarial Circuit Anomaly Detection and Evaluation