Abstract: With the continuous expansion of automation and unmanned demand in industry, hydraulic cylinders, as one of the most important industrial actuators, urgently need fault diagnosis solutions ...
Abstract: Neural networks that overlook the underlying causal relationships among observed variables pose significant risks in high-stakes decision-making contexts due to concerns about the robustness ...
ABSTRACT: A degenerative neurological condition called Parkinson disease (PD) that evolves progressively, making detection difficult. A neurologist requires a clear healthcare history from the ...
ABSTRACT: This paper aims to investigate the effectiveness of logistic regression and discriminant analysis in predicting diabetes in patients using a diabetes dataset. Additionally, the paper ...
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.5c01525. Efficiency analysis of different normalization strategies ...
Dharmesh Tailor, Alvaro H.C. Correia, Eric Nalisnick and Christos Louizos. "Approximating Full Conformal Prediction for Neural Network Regression with Gauss-Newton Influence." [ICLR2025] Qualcomm AI ...
The goal of a machine learning regression problem is to predict a single numeric value. Quantile regression is a variation where you are concerned with under-prediction or over-prediction. I'll phrase ...
Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of neural network quantile regression. The goal of a quantile regression problem is to predict a single numeric ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results