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

Interpretable machine learning offers insights into what factors drive acertain prediction of a black-box system. A large number of interpretingmethods focus on identifying explanatory input features, which generally fallinto two main categories: attribution and selection. A popularattribution-based approach is to exploit local neighborhoods for learninginstance-specific explainers in an additive manner. The process is thusinefficient and susceptible to poorly-conditioned samples. Meanwhile, manyselection-based methods directly optimize local feature distributions in aninstance-wise training framework, thereby being capable of leveraging globalinformation from other inputs. However, they can only interpret single-classpredictions and many suffer from inconsistency across different settings, dueto a strict reliance on a pre-defined number of features selected. This workexploits the strengths of both methods and proposes a framework for learninglocal explanations simultaneously for multiple target classes. Our modelexplainer significantly outperforms additive and instance-wise counterparts onfaithfulness with more compact and comprehensible explanations. We alsodemonstrate the capacity to select stable and important features throughextensive experiments on various data sets and black-box model architectures.

An Additive Instance-Wise Approach to Multi-class Model Interpretation