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Found 6 papers, 2 papers with code
Objective Evaluation of Deep Uncertainty Predictions for COVID-19 Detection
Accordingly, uncertainty quantification methods are capable of flagging risky predictions with high uncertainty estimates.
Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991-2020)
We have little knowledge about the optimal treatment methods as there are many sources of uncertainty in medical science.
An Uncertainty-aware Transfer Learning-based Framework for Covid-19 Diagnosis
The early and reliable detection of COVID-19 infected patients is essential to prevent and limit its outbreak.
SpinalNet: Deep Neural Network with Gradual Input
Traditional learning with ImageNet pre-trained initial weights and SpinalNet classification layers provided the SOTA performance on STL-10, Fruits 360, Bird225, and Caltech-101 datasets.
Ranked #1 on
Satellite Image Classification
on STL-10, 40 Labels
Fine-Grained Image Classification
Satellite Image Classification
+1
Optimal Uncertainty-guided Neural Network Training
Most of the existing cost functions of uncertainty guided NN training are not customizable and the convergence of training is uncertain.
Enhanced Multiobjective Evolutionary Algorithm based on Decomposition for Solving the Unit Commitment Problem
The unit commitment (UC) problem is a nonlinear, high-dimensional, highly constrained, mixed-integer power system optimization problem and is generally solved in the literature considering minimizing the system operation cost as the only objective.
