Incorporating Uncertainty Quantification for the Performance Improvement of Academic Recommenders

Incorporating Uncertainty Quantification for the Performance Improvement of Academic Recommenders

Deep learning is widely used in many real-life applications. Despite their remarkable performance accuracies, deep learning networks are often poorly calibrated, which could be harmful in risk-sensitive scenarios. Uncertainty quantification offers a way to evaluate the reliability and trustworthines...

Full description

Bibliographic Details
Main Authors:	Jie Zhu, Luis Leon Novelo, Ashraf Yaseen
Format:	Article
Language:	English
Published:	MDPI AG 2023-06-01
Series:	Knowledge
Subjects:	uncertainty quantification Monte Carlo dropout ensemble recommender systems BERT temporal graph networks
Online Access:	https://www.mdpi.com/2673-9585/3/3/20

Similar Items

Leveraging Monte Carlo Dropout for Uncertainty Quantification in Real-Time Object Detection of Autonomous Vehicles
by: Rui Zhao, et al.
Published: (2024-01-01)

Ranking Information Extracted from Uncertainty Quantification of the Prediction of a Deep Learning Model on Medical Time Series Data
by: Ruxandra Stoean, et al.
Published: (2020-07-01)

Derivation and Uncertainty Quantification of a Data-Driven Subcooled Boiling Model
by: Jerol Soibam, et al.
Published: (2020-11-01)

Uncertainty Quantification through Dropout in Time Series Prediction by Echo State Networks
by: Miguel Atencia, et al.
Published: (2020-08-01)

Uncertainty quantification of a deep learning fuel property prediction model
by: Kiran K. Yalamanchi, et al.
Published: (2023-12-01)

Automated Detection of Presymptomatic Conditions in Spinocerebellar Ataxia Type 2 Using Monte Carlo Dropout and Deep Neural Network Techniques with Electrooculogram Signals
by: Catalin Stoean, et al.
Published: (2020-05-01)

Epistemic Uncertainty and Model Transparency in Rock Facies Classification Using Monte Carlo Dropout Deep Learning
by: Touhid Mohammad Hossain, et al.
Published: (2023-01-01)

Uncertainty Quantification of Compressor Map Using the Monte Carlo Approach Accelerated by an Adjoint-Based Nonlinear Method
by: Shenren Xu, et al.
Published: (2023-03-01)

Uncertainty Quantification for MLP-Mixer Using Bayesian Deep Learning
by: Abdullah A. Abdullah, et al.
Published: (2023-04-01)

Parametric Metamodeling Based on Optimal Transport Applied to Uncertainty Evaluation
by: Sergio Torregrosa, et al.
Published: (2024-02-01)

Uncertainty quantification of the flexural strengthening of RC beams with UHPC using improved metaheuristic algorithm
by: Nageh M. Ali, et al.
Published: (2024-05-01)

Uncertainty quantification in production forecast for shale gas well using a semi-analytical model
by: Bingxiang Xu, et al.
Published: (2018-12-01)

Fast Uncertainty Quantification of Electromechanical Oscillation Frequency on Varying Generator Damping
by: Yongli Zhu, et al.
Published: (2023-01-01)

Reliability analysis and uncertainty quantification of clay and sand slopes stability evaluated by Fellenius and Bishop’s simplified methods
by: Nhu Son Doan
Published: (2023-11-01)

DTCRSKG: A Deep Travel Conversational Recommender System Incorporating Knowledge Graph
by: Hui Fang, et al.
Published: (2022-04-01)

Uncertainty Evaluation of a Gas Turbine Model Based on a Nonlinear Autoregressive Exogenous Model and Monte Carlo Dropout
by: Armando Cajahuaringa, et al.
Published: (2024-01-01)

Addressing Uncertainty in Tool Wear Prediction with Dropout-Based Neural Network
by: Arup Dey, et al.
Published: (2023-09-01)

Bayesian Activity Estimation and Uncertainty Quantification of Spent Nuclear Fuel Using Passive Gamma Emission Tomography
by: Ahmed Karam Eldaly, et al.
Published: (2021-10-01)

High-Dimensional Uncertainty Quantification in Electrical Impedance Tomography Forward Problem Based on Deep Neural Network
by: Yingge Zhao, et al.
Published: (2023-01-01)

Application of Generalized Polynomial Chaos for Quantification of Uncertainties of Time Averages and Their Sensitivities in Chaotic Systems
by: Kyriakos Dimitrios Kantarakias, et al.
Published: (2020-04-01)

On off-line and on-line Bayesian filtering for uncertainty quantification of structural deterioration
by: Antonios Kamariotis, et al.
Published: (2023-01-01)

Uncertainty Quantification in Geochemical Mapping: A Review and Recommendations
by: J. Wang, et al.
Published: (2024-03-01)

Uncertainty quantification methodology for model parameters in sub-channel codes using MCMC sampling
by: HE Xin, et al.
Published: (2023-12-01)

McCARD/MIG stochastic sampling calculations for nuclear cross section sensitivity and uncertainty analysis
by: Ho Jin Park
Published: (2022-11-01)

Probabilistic Intraday PV Power Forecast Using Ensembles of Deep Gaussian Mixture Density Networks
by: Oliver Doelle, et al.
Published: (2023-01-01)

Performing linear regression with responses calculated using Monte Carlo transport codes
by: Dean Price, et al.
Published: (2022-05-01)

Monte Carlo Dropout for Uncertainty Estimation and Motor Imagery Classification
by: Daily Milanés-Hermosilla, et al.
Published: (2021-10-01)

EEG-Based Seizure Prediction via Model Uncertainty Learning
by: Chang Li, et al.
Published: (2023-01-01)

Uncertainty Quantification for Numerical Solutions of the Nonlinear Partial Differential Equations by Using the Multi-Fidelity Monte Carlo Method
by: Wenting Du, et al.
Published: (2022-07-01)

Course Recommendation Model Based on Layer Dropout Graph Differential Contrastive Learning
by: Yong Ouyang, et al.
Published: (2024-01-01)

Leveraging Bayesian deep learning and ensemble methods for uncertainty quantification in image classification: A ranking-based approach
by: Abdullah A. Abdullah, et al.
Published: (2024-01-01)

Computation of a probabilistic uranium concentration map of Norway: A digital expert elicitation approach employing random forests and artificial neural networks
by: Hendrik Paasche, et al.
Published: (2023-11-01)

UQpy v4.1: Uncertainty quantification with Python
by: Dimitrios Tsapetis, et al.
Published: (2023-12-01)

HybridBERT4Rec: A Hybrid (Content-Based Filtering and Collaborative Filtering) Recommender System Based on BERT
by: Chanapa Channarong, et al.
Published: (2022-01-01)

Efficient Adaptive Monte Carlo Uncertainty Forecasting for High Dimensional Nonlinear Dynamic Systems
by: Andrew W. Vanfossen, et al.
Published: (2023-01-01)

Modelling and quantification of measurement uncertainty for leak localisation in a gas pipeline
by: Runchang Chen, et al.
Published: (2022-12-01)

A review of uncertainty quantification and its applications in numerical simulation of scramjet combustor
by: Linying Li, et al.
Published: (2023-05-01)

Influence of the Thermal Conductivity and Ambient Temperature Uncertainty on the Heat Losses through the External Wall
by: Marcin Koniorczyk, et al.
Published: (2021-02-01)

Uncertainty Quantification of Trajectory Clustering Applied to Ocean Ensemble Forecasts
by: Guilherme S. Vieira, et al.
Published: (2020-10-01)

Uncertainty Quantification in Energy Management Procedures
by: Luca Giaccone, et al.
Published: (2020-09-01)