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Found 8 papers, 4 papers with code
Improving MC-Dropout Uncertainty Estimates with Calibration Error-based Optimization
Uncertainty quantification of machine learning and deep learning methods plays an important role in enhancing trust to the obtained result.
MCUa: Multi-level Context and Uncertainty aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification
It exploits the high sensitivity to the multi-level contextual information using an uncertainty quantification component to accomplish a novel dynamic ensemble model. MCUamodelhas achieved a high accuracy of 98. 11% on a breast cancer histology image dataset.
Breast Cancer Histology Image Classification
Classification
+1
UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection
Differently from most of existing studies, which used either CT scan or X-ray images in COVID-19-case classification, we present a simple but efficient deep learning feature fusion model, called UncertaintyFuseNet, which is able to classify accurately large datasets of both of these types of images.
A Review of Generalized Zero-Shot Learning Methods
Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples under the condition that some output classes are unknown during supervised learning.
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
Uncertainty quantification (UQ) plays a pivotal role in reduction of uncertainties during both optimization and decision making processes.
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.
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
Image Classification
on Kuzushiji-MNIST
Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning
Continuous control tasks in reinforcement learning are important because they provide an important framework for learning in high-dimensional state spaces with deceptive rewards, where the agent can easily become trapped into suboptimal solutions.
