PEID decomposes the causal effect of multiple sources on a target under maximum-entropy interventions into unique and synergistic information, enabling hyperedge causal graphs and downward causation analysis.
Estimating the mean and variance of the target probability distribution
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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The Robust 4D Visual Geometry Transformer with Uncertainty-Aware Priors outperforms prior methods on dynamic benchmarks by cutting Mean Accuracy error 13.43% and raising segmentation F-measure 10.49% via three uncertainty mechanisms while keeping feed-forward speed.
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Partial Effective Information Decomposition for Synergistic Causality
PEID decomposes the causal effect of multiple sources on a target under maximum-entropy interventions into unique and synergistic information, enabling hyperedge causal graphs and downward causation analysis.
-
Robust 4D Visual Geometry Transformer with Uncertainty-Aware Priors
The Robust 4D Visual Geometry Transformer with Uncertainty-Aware Priors outperforms prior methods on dynamic benchmarks by cutting Mean Accuracy error 13.43% and raising segmentation F-measure 10.49% via three uncertainty mechanisms while keeping feed-forward speed.