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arXiv research

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.

168,657 papers · 148 categories

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4 results for “quality-driven”

Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensemblesstat.ML

The paper presents a method for generating well-calibrated prediction intervals using quality-driven deep ensembles.

problem Generating reliable prediction intervals for regression analysis.

method A multi-objective loss function combining quality measures for prediction intervals and point estimates, with a penalty function to ensure semantic integrity and stability.

result The method produces well-calibrated prediction intervals and point estimates, capturing both aleatoric and epistemic uncertainty.

prediction intervals deep ensembles quality-driven multi-objective loss split normal mixture

2020-07-19abs ↗pdf ↗

Deep Learning Based Power Control for Quality-Driven Wireless Video Transmissionscs.LG

In this paper, wireless video transmission to multiple users under total transmission power and minimum required video quality constraints is studied. In order to provide the desired performance levels to the end-users in real-time video transmissions while using the energy resources efficiently, we assume that power c…

2018-10-16abs ↗pdf ↗

Dual Accuracy-Quality-Driven Neural Network for Prediction Interval Generationcs.LG

This paper presents a method to automatically generate high-quality prediction intervals for neural networks.

problem Accurate uncertainty quantification for deep learning models in real-world applications.

method Dual neural network approach with a novel loss function to balance prediction interval width and coverage.

result Our method produces significantly narrower prediction intervals with higher probability coverage compared to state-of-the-art methods.

prediction intervals neural networks regression uncertainty quantification machine learning

2022-12-13abs ↗pdf ↗

Learning Complex Physical Regimes via Coverage-oriented Uncertainty Quantification: An application to the Critical Heat Fluxcs.LG

New ML methods improve physical system understanding by quantifying uncertainty across diverse regimes.

problem Capturing multi-regime physical systems with standard ML techniques.

method Coverage-oriented uncertainty quantification (UQ) methods.

result Coverage-oriented UQ models deliver physically consistent uncertainty estimates.

Critical Heat Flux Uncertainty Quantification Coverage-oriented UQ Multi-Physics Scientific Machine Learning

2026-02-25abs ↗pdf ↗