Expected improvement for expensive optimization: a review
The expected improvement (EI) algorithm is a very popular method for expensive
optimization problems. In the past twenty years, the EI criterion has been extended to deal …
optimization problems. In the past twenty years, the EI criterion has been extended to deal …
Eight years of AutoML: categorisation, review and trends
Abstract Knowledge extraction through machine learning techniques has been successfully
applied in a large number of application domains. However, apart from the required …
applied in a large number of application domains. However, apart from the required …
Bayesian optimization for accelerating hyper-parameter tuning
V Nguyen - 2019 IEEE second international conference on …, 2019 - ieeexplore.ieee.org
Bayesian optimization (BO) has recently emerged as a powerful and flexible tool for hyper-
parameter tuning and more generally for the efficient global optimization of expensive black …
parameter tuning and more generally for the efficient global optimization of expensive black …
Assessment of stacked unidirectional and bidirectional long short-term memory networks for electricity load forecasting
S Atef, AB Eltawil - Electric Power Systems Research, 2020 - Elsevier
Electricity load forecasting has been a substantial problem in the electric power system
management process. An accurate forecasting model is essential to avoid imprecise …
management process. An accurate forecasting model is essential to avoid imprecise …
Toward a digital twin: time series prediction based on a hybrid ensemble empirical mode decomposition and BO-LSTM neural networks
W Hu, Y He, Z Liu, J Tan… - Journal of …, 2021 - asmedigitalcollection.asme.org
Precise time series prediction serves as an important role in constructing a digital twin (DT).
The various internal and external interferences result in highly nonlinear and stochastic time …
The various internal and external interferences result in highly nonlinear and stochastic time …
A deep bi-directional long-short term memory neural network-based methodology to enhance short-term electricity load forecasting for residential applications
Unexpected fluctuations associated with electricity load consumption patterns pose a
significant threat to the stability, efficiency, and sustainability of modernized energy systems …
significant threat to the stability, efficiency, and sustainability of modernized energy systems …
Knowing the what but not the where in Bayesian optimization
V Nguyen, MA Osborne - International Conference on …, 2020 - proceedings.mlr.press
Bayesian optimization has demonstrated impressive success in finding the optimum input x
$\ast $ and output f $\ast $= f (x $\ast $)= max f (x) of a black-box function f. In some …
$\ast $ and output f $\ast $= f (x $\ast $)= max f (x) of a black-box function f. In some …
Cost-informed Bayesian reaction optimization
Bayesian optimization (BO) is an efficient method for solving complex optimization problems,
including those in chemical research, where it is gaining significant popularity. Although …
including those in chemical research, where it is gaining significant popularity. Although …
Optimizing the performance of convolutional neural network for enhanced gesture recognition using sEMG
Deep neural networks (DNNs) have demonstrated higher performance results when
compared to traditional approaches for implementing robust myoelectric control (MEC) …
compared to traditional approaches for implementing robust myoelectric control (MEC) …
An integrated approach for machine-learning-based system identification of dynamical systems under control: application towards the model predictive control of a …
Advanced model-based control strategies, eg, model predictive control, can offer superior
control of key process variables for multiple-input multiple-output systems. The quality of the …
control of key process variables for multiple-input multiple-output systems. The quality of the …