A comparative study on the most effective machine learning model for blast loading prediction: From GBDT to Transformer

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	Year	Citations
Deep Residual Learning for Image Recognition Kaiming He, Xiangyu Zhang, Shaoqing Ren, Image ClassificationDeep Neural NetworksMachine VisionImage AnalysisMachine Learning	2016	214.9K
MizAR 60 for Mizar 50 DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)	2023	73.5K
Scikit-learn: Machine Learning in Python Fabián Pedregosa, Gaël Varoquaux, Alexandre Gramfort, arXiv (Cornell University)	2012	63.3K
AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)	2018	45.3K
XGBoost Cluster ComputingEngineeringMachine LearningMachine Learning ToolInformation Retrieval	2016	44.2K
Dropout: a simple way to prevent neural networks from overfitting Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky,	2014	34.2K
LightGBM: A Highly Efficient Gradient Boosting Decision Tree Guolin Ke, Qi Meng, Thomas Finley, HAL (Le Centre pour la Communication Scientifique Directe) Artificial IntelligenceEngineeringMachine LearningSmall GradientsMachine Learning Tool	2017	9.5K
Decoupled Weight Decay Regularization Ilya Loshchilov, Frank Hutter arXiv (Cornell University) EngineeringMachine LearningWeight DecayAtomic DecompositionImage Analysis	2017	9K
Optuna Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Hyperparameter EstimationNew DesignEngineeringModel TuningOptimization Software	2019	6.4K
A Short Introduction to Boosting Yoav Freund, Robert E. Schapire	1999	3K

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