Researchers from Stanford University and Rensselaer Polytechnic Institute have developed an advanced AI model that improves the prediction accuracy of clinical trial approvals. The study, published in ...

Polypharmacology, a single drug that targets multiple proteins, holds promise for addressing unmet medical needs. Achieving accurate, reliable and scalable predictions of protein–ligand binding ...

A new technique can help researchers who use Bayesian inference achieve more accurate results more quickly, without a lot of additional work. Pollsters trying to predict presidential election results ...

This study applies conformal prediction (CP) to SpineNet to quantify prediction uncertainty in the classification of central canal stenosis (CCS) into four grades: normal, mild, moderate, and severe.

This conference is being held in cooperation with the American Statistical Association (ASA) and GAMM Activity Group on Uncertainty Quantification (GAMM AG UQ). Uncertainty quantification (UQ) is ...

Lakkaraju, Himabindu, Sree Harsha Tanneru, and Chirag Agarwal. "Quantifying Uncertainty in Natural Language Explanations of Large Language Models." Paper presented at the Society for Artificial ...

We live in an age defined by volatility, uncertainty, complexity, and ambiguity. In such an environment, risk is no longer a peripheral concern delegated to compliance teams or internal auditors. It ...

Prognostic Significance of Isolated Tumor Cells and the Role of Immunohistochemistry in Nodal Evaluation in Breast Cancer: A SEER-Based Analysis and Reappraisal We used Monte Carlo simulation methods ...

Quantifying uncertainty in carbon accounting is essential at scales ranging from individual projects to country-level compensation for reducing emissions from deforestation and forest degradation.

The accurate determination of protein concentration is a foundational requirement in molecular biology, proteomics, and clinical diagnostics. Choosing the most appropriate protein quantification ...

Harsha Tanneru, Sree, Chirag Agarwal, and Himabindu Lakkaraju. "Quantifying Uncertainty in Natural Language Explanations of Large Language Models." Proceedings of the International Conference on ...