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Found 9 papers, 3 papers with code
Gradient-flow adaptive importance sampling for Bayesian leave one out cross-validation for sigmoidal classification models
We introduce a set of gradient-flow-guided adaptive importance sampling (IS) transformations to stabilize Monte-Carlo approximations of point-wise leave one out cross-validated (LOO) predictions for Bayesian classification models.
Robust-GBDT: GBDT with Nonconvex Loss for Tabular Classification in the Presence of Label Noise and Class Imbalance
This research heralds a paradigm shift in machine learning, paving the way for a new era of robust and precise classification across diverse real-world applications.
NCART: Neural Classification and Regression Tree for Tabular Data
Deep learning models have become popular in the analysis of tabular data, as they address the limitations of decision trees and enable valuable applications like semi-supervised learning, online learning, and transfer learning.
TRBoost: A Generic Gradient Boosting Machine based on Trust-region Method
Gradient Boosting Machines (GBMs) have demonstrated remarkable success in solving diverse problems by utilizing Taylor expansions in functional space.
Multi-objective optimization and explanation for stroke risk assessment in Shanxi province
The experimental results showed that the QIDNN model with 7 interactive features achieve the state-of-art accuracy $83. 25\%$.
Analysis and classification of main risk factors causing stroke in Shanxi Province
Evaluation of the risk of getting stroke is important for the prevention and treatment of stroke in China.
A phase field model for mass transport with semi-permeable interfaces
Numerical simulations first illustrate the consistency of theoretical results on the sharp interface limit.
Numerical Analysis Numerical Analysis 76Z99, 92B05, 76R50
An Energy Stable C0 Finite Element Scheme for A Phase-Field Model of Vesicle Motion and Deformation
A thermodynamically consistent phase-field model is introduced for simulating motion and shape transformation of vesicles under flow conditions.
Numerical Analysis Numerical Analysis 76T06 92B05 35Q92
Neural-PDE: A RNN based neural network for solving time dependent PDEs
Partial differential equations (PDEs) play a crucial role in studying a vast number of problems in science and engineering.
