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Enhancing LLM Confidence: A New Approach Using Reasoning Path Geometry and Uncertainty Analysis

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Enhancing Carbon Storage Models with AI: A New Approach to Geological Data Assimilation

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Navigating Healthcare AI: A Pre-Clinical Roadmap for Trust and Reliability

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Adapting Machine Learning for Fusion Energy: An Online Ensemble Approach

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Predicting Air Quality with Incomplete Data: A Deep Learning Solution for Reliable Forecasts

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Enhanced System Monitoring: Using Kullback–Leibler Divergence for Reliable Identification

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Charting a Course for Reliable, Robust, and Secure Quantum Machine Learning

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Unpacking How AI Models Resolve Conflicting Information from Images and Text

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Unlocking Reliable AI in Medical Imaging with Limited Data: A New Theoretical Framework

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Biology-Informed Machine Learning: Adapting AI for Life Sciences’ Unique Challenges

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Bridging Bayesian and Frequentist Views on Machine Learning Uncertainty

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AI Models That Understand Their Own Ignorance: A New Era of Trustworthy Machine Learning

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Variational Pólya Tree: Bridging Bayesian Nonparametrics with Deep Generative Models

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Autonomous Vehicles Gain Self-Awareness: Predicting the Road Ahead with Quantified Uncertainty

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Adaptive Prediction for Self-Evolving AI: Quantifying LLM Confidence

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Enhancing Decision-Making: A Framework for Human-AI Uncertainty Collaboration

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New AI Models Predict Complex Systems with Physical Laws and Uncertainty Awareness

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Advancing Molecular Discovery: Uncertainty-Aware AI for Multi-Objective 3D Design

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Improving Language Model Uncertainty Estimates Through Diverse Sampling

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Deconstructing Performance Gaps in AI’s Selective Classifiers

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Advancing Surface Metrology with Deep Learning and Uncertainty Quantification

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Mapping the Early Universe: Bayesian Deep Learning Uncovers Primordial Magnetic Fields from CMB Data

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Assessing AI’s Reasoning Capabilities in Energy System Analysis: A New Benchmark

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Enhancing AI Reliability: A Dual Approach to Mitigate Overconfident Errors

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A New Approach to Bayesian Meta-Learning: Neural Variational Dropout Processes

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Closed-Loop Uncertainty Modeling Improves Autonomous Trajectory Planning

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Advanced AI Models Enhance Sub-seasonal Wind Speed Forecasts for Renewable Energy

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Quantifying Uncertainty in Machine Learning for High-Stakes Applications

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