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New Target-Response Jacobian metric outperforms influence measures in AI model analysis

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced the target-response Jacobian ($J$), a new metric for understanding how model predictions change in response to alterations in individual training targets. This metric complements existing training-data attribution (TDA) methods by focusing on target sensitivity rather than observation weights. Experiments on various datasets and network architectures, including tanh networks and an MNIST classifier, demonstrated that $J$ significantly outperforms traditional influence measures in predicting target perturbations, achieving near-perfect $R^2$ scores where influence measures failed. AI

IMPACT Introduces a novel metric for analyzing model behavior, potentially improving interpretability and debugging.

RANK_REASON Academic paper introducing a new technical concept and experimental results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Target-Response Jacobian metric outperforms influence measures in AI model analysis

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Academic paper introducing a new technical concept and experimental results. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Paul Geertsema, Simon Geertsema ·

    Influence Is the Target-Response Jacobian Times Residuals

    arXiv:2610.27689v1 Announce Type: new Abstract: We introduce the target-response Jacobian $J = [\partial \hat y_i/\partial y_j]$, which measures how model predictions respond to changes in individual training targets. $J$ complements training-data attribution (TDA) methods, which…

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