XtrAIn shifts occlusion from input space to parameter space along the training trajectory to produce cleaner feature attributions than standard methods.
Explainable artificial intelligence (xai): From inherent explainability to large language models.arXiv preprint arXiv:2501.09967
5 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 5representative citing papers
The paper introduces the Agentic Risk Standard (ARS) as a payment settlement framework that delivers predefined compensation for AI agent execution failures, misalignment, or unintended outcomes.
GRAPHIC interprets confusion matrices from linear classifiers on intermediate layers as graphs to visualize and quantify class confusion dynamics in deep learning.
ERPPO adds a DSA-based ambiguity estimator to MAPPO and switches between L1 and L2 entropy regularization to improve exploration and stability in non-stationary multi-dimensional observations.
Automated audit of 4514 omics papers and major datasets shows severe underreporting of demographics and European-ancestry dominance, risking bias amplification in biomedical foundation models.
citing papers explorer
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XtrAIn: Training-Guided Occlusion for Feature Attribution
XtrAIn shifts occlusion from input space to parameter space along the training trajectory to produce cleaner feature attributions than standard methods.
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Quantifying Trust: Financial Risk Management for Trustworthy AI Agents
The paper introduces the Agentic Risk Standard (ARS) as a payment settlement framework that delivers predefined compensation for AI agent execution failures, misalignment, or unintended outcomes.
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The Confusion is Real: GRAPHIC -- A Network Science Approach to Confusion Matrices in Deep Learning
GRAPHIC interprets confusion matrices from linear classifiers on intermediate layers as graphs to visualize and quantify class confusion dynamics in deep learning.
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ERPPO: Entropy Regularization-based Proximal Policy Optimization
ERPPO adds a DSA-based ambiguity estimator to MAPPO and switches between L1 and L2 entropy regularization to improve exploration and stability in non-stationary multi-dimensional observations.
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Perspective on Bias in Biomedical AI: Preventing Downstream Healthcare Disparities
Automated audit of 4514 omics papers and major datasets shows severe underreporting of demographics and European-ancestry dominance, risking bias amplification in biomedical foundation models.