Latest Articles for Uncertainty

Advances in creating more accurate vascular models considering uncertainties in medical imaging.

Study reveals star movement patterns and dark matter insights in our galaxy.

Introducing a probabilistic approach to assess GNN explanations for better reliability.

Examining how AI uncertainty affects decision-making in healthcare.

A look at robust shortest path problems in uncertain network conditions.

A new approach to improve prediction confidence in regression tasks.

An overview of Bayesian neural networks and their importance in AI.

Examining how random sets and belief functions affect decision-making in various fields.

ProvNeRF improves 3D scene representation using limited images by analyzing point origins.

A framework for stable, reliable power systems with renewable resources.

Examining the challenges of predicting tipping points in natural systems due to climate change.

Deep Kernel Learning enhances control systems, ensuring safety and reliability amidst complexity.

A new system optimizes language models for faster, more efficient responses.

A systematic approach to developing and using indexes in research.

A method to enhance reliability in text generation by measuring uncertainty.

How muscle co-contraction helps us deal with unpredictable movements.

A guide to making informed decisions during disease outbreaks using optimization techniques.

New methods improve safety of systems in uncertain environments.

Innovative methods aim to improve measurements of the Universe's expansion rate.

Research highlights the role of uncertainty in asking clarification questions in dialogue systems.

Advancements in AI focus on assessing uncertainty in robotic object detection.

New method enhances model predictions by addressing miscalibration issues effectively.

This study enhances fine-tuning efficiency in neural networks using transductive active learning.

Explore how uncertainty affects machine learning predictions and methods to measure it.

Experts develop models to predict disease spread amid uncertainties.

A look at control systems and the role of state estimation in technology management.

Credal learning theory offers new insights for adapting machine learning models to changing data.

A new framework helps analyze explanations from large language models effectively.

Learn how SMPC balances safety and performance in uncertain systems.

A new method improves flow analysis by addressing uncertainty in FTLE data.

This research evaluates AI model confidence and explanation quality in noisy environments.

Enhancing safety in machines through better perception contracts and learning-based approaches.

This study highlights the importance of uncertainty in assessing Vision-Language Models.

Examining data analysis pipelines and methods to manage uncertainty in political research.

Research focuses on improving explanations and handling uncertainty in AV decision-making.

A look into the reliability of machine learning and deep neural networks.

Improving shear bias calibration is key to studying dark energy and the universe's expansion.

A new method to evaluate the accuracy of LLM outputs using local intrinsic dimensions.

New methods enhance aircraft path predictions for safer air traffic control.

A new model improves predictions for heavy rainfall events.