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

Is critical input information encoded in specific sparse pathways within theneural network? In this work, we discuss the problem of identifying thesecritical pathways and subsequently leverage them for interpreting the network'sresponse to an input. The pruning objective -- selecting the smallest group ofneurons for which the response remains equivalent to the original network --has been previously proposed for identifying critical pathways. We demonstratethat sparse pathways derived from pruning do not necessarily encode criticalinput information. To ensure sparse pathways include critical fragments of theencoded input information, we propose pathway selection via neurons'contribution to the response. We proceed to explain how critical pathways canreveal critical input features. We prove that pathways selected via neuroncontribution are locally linear (in an L2-ball), a property that we use forproposing a feature attribution method: "pathway gradient". We validate ourinterpretation method using mainstream evaluation experiments. The validationof pathway gradient interpretation method further confirms that selectedpathways using neuron contributions correspond to critical input features. Thecode is publicly available.

Neural Response Interpretation through the Lens of Critical Pathways