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
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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.
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.