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A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

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1 result for “CRP-O”
Distribution free uncertainty quantification in neuroscience-inspired deep operatorsstat.ML

Paper introduces CRP-O framework for uncertainty quantification in deep operators.

problem Uncertainty quantification in energy-efficient deep learning algorithms, especially in SNNs.
method CRP-O framework using RP networks and SCP, with Gaussian Process Regression for super-resolution.
result Enhanced uncertainty bounds improve UQ estimates compared to existing methods.
Uncertainty quantificationDeep learningSNNsCRP-OGaussian Process
2024-12-12abs ↗pdf ↗