[HTML][HTML] Condition monitoring using machine learning: A review of theory, applications, and recent advances
In modern industry, the quality of maintenance directly influences equipment's operational
uptime and efficiency. Hence, based on monitoring the condition of the machinery, predictive …
uptime and efficiency. Hence, based on monitoring the condition of the machinery, predictive …
A review of hydrodynamic and machine learning approaches for flood inundation modeling
Machine learning (also called data-driven) methods have become popular in modeling flood
inundations across river basins. Among data-driven methods, traditional machine learning …
inundations across river basins. Among data-driven methods, traditional machine learning …
Gradient projection memory for continual learning
The ability to learn continually without forgetting the past tasks is a desired attribute for
artificial learning systems. Existing approaches to enable such learning in artificial neural …
artificial learning systems. Existing approaches to enable such learning in artificial neural …
Prediction of maximum tensile stress in plain-weave composite laminates with interacting holes via stacked machine learning algorithms: A comparative study
Plain weave composite is a long-lasting type of fabric composite that is stable enough when
being handled. Open-hole composites have been widely used in industry, though they have …
being handled. Open-hole composites have been widely used in industry, though they have …
Artificial cognition: How experimental psychology can help generate explainable artificial intelligence
JET Taylor, GW Taylor - Psychonomic Bulletin & Review, 2021 - Springer
Artificial intelligence powered by deep neural networks has reached a level of complexity
where it can be difficult or impossible to express how a model makes its decisions. This …
where it can be difficult or impossible to express how a model makes its decisions. This …
Data augmented flatness-aware gradient projection for continual learning
The goal of continual learning (CL) is to continuously learn new tasks without forgetting
previously learned old tasks. To alleviate catastrophic forgetting, gradient projection based …
previously learned old tasks. To alleviate catastrophic forgetting, gradient projection based …
Gradients without backpropagation
Using backpropagation to compute gradients of objective functions for optimization has
remained a mainstay of machine learning. Backpropagation, or reverse-mode differentiation …
remained a mainstay of machine learning. Backpropagation, or reverse-mode differentiation …
[图书][B] Machine learning: a first course for engineers and scientists
This book introduces machine learning for readers with some background in basic linear
algebra, statistics, probability, and programming. In a coherent statistical framework it covers …
algebra, statistics, probability, and programming. In a coherent statistical framework it covers …
A u-turn on double descent: Rethinking parameter counting in statistical learning
A Curth, A Jeffares… - Advances in Neural …, 2024 - proceedings.neurips.cc
Conventional statistical wisdom established a well-understood relationship between model
complexity and prediction error, typically presented as a _U-shaped curve_ reflecting a …
complexity and prediction error, typically presented as a _U-shaped curve_ reflecting a …
[HTML][HTML] Ensemble Machine Learning approach for evaluating the material characterization of carbon nanotube-reinforced cementitious composites
F Bagherzadeh, T Shafighfard - Case Studies in Construction Materials, 2022 - Elsevier
Time and cost-efficient techniques are essential to avoid extra conventional experimental
studies with large data-set for material characterization of composite materials. This study is …
studies with large data-set for material characterization of composite materials. This study is …