[HTML][HTML] A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …
uncertainties during both optimization and decision making processes. They have been …
[HTML][HTML] Landslide susceptibility mapping using machine learning: A literature survey
Landslide is a devastating natural disaster, causing loss of life and property. It is likely to
occur more frequently due to increasing urbanization, deforestation, and climate change …
occur more frequently due to increasing urbanization, deforestation, and climate change …
Predictive Performances of ensemble machine learning algorithms in landslide susceptibility mapping using random forest, extreme gradient boosting (XGBoost) and …
T Kavzoglu, A Teke - Arabian Journal for Science and Engineering, 2022 - Springer
Across the globe, landslides have been recognized as one of the most detrimental
geological calamities, especially in hilly terrains. However, the correct determination of …
geological calamities, especially in hilly terrains. However, the correct determination of …
Machine learning for predicting battery capacity for electric vehicles
Predicting the evolution of multiphysics battery systems face severe challenges, including
various aging mechanisms, cell-to-cell variation and dynamic operating conditions. Despite …
various aging mechanisms, cell-to-cell variation and dynamic operating conditions. Despite …
Battery prognostics and health management from a machine learning perspective
Transportation electrification is gaining prominence as a significant pathway for reducing
emissions and enhancing environmental sustainability. Central to this shift are lithium-ion …
emissions and enhancing environmental sustainability. Central to this shift are lithium-ion …
Kaggle forecasting competitions: An overlooked learning opportunity
CS Bojer, JP Meldgaard - International Journal of Forecasting, 2021 - Elsevier
We review the results of six forecasting competitions based on the online data science
platform Kaggle, which have been largely overlooked by the forecasting community. In …
platform Kaggle, which have been largely overlooked by the forecasting community. In …
Bayesian estimation of gene constraint from an evolutionary model with gene features
Measures of selective constraint on genes have been used for many applications, including
clinical interpretation of rare coding variants, disease gene discovery and studies of genome …
clinical interpretation of rare coding variants, disease gene discovery and studies of genome …
[PDF][PDF] Nas-bench-301 and the case for surrogate benchmarks for neural architecture search
ABSTRACT Neural Architecture Search (NAS) is a logical next step in the automatic learning
of representations, but the development of NAS methods is slowed by high computational …
of representations, but the development of NAS methods is slowed by high computational …
Machine learning models to accelerate the design of polymeric long-acting injectables
Long-acting injectables are considered one of the most promising therapeutic strategies for
the treatment of chronic diseases as they can afford improved therapeutic efficacy, safety …
the treatment of chronic diseases as they can afford improved therapeutic efficacy, safety …
How powerful are performance predictors in neural architecture search?
Early methods in the rapidly developing field of neural architecture search (NAS) required
fully training thousands of neural networks. To reduce this extreme computational cost …
fully training thousands of neural networks. To reduce this extreme computational cost …