Artificial neural networks

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Calibration of Pre-trained Transformers (2020, background): “Neural networks have seen wide adoption but are frequently criticized for being black boxes, offering little insight as to why predictions are made (Benı́tez et al., 1997; Dayhoff and DeLeo, 2001; Castelvecchi, 2016) and making it difficult to diagnose errors at test-time.”
Accurate and Precise Prediction of Soil Properties from a Large Mid-Infrared Spectral Library (2019, methodology): “ANNs use a supervised learning approach to fit the relationship between predictor and response variables by adjusting the weights through an optimization process until the error between the observed and predicted value is minimized [40].”
The use of artificial neural networks to analyze and predict alongshore sediment transport (2010, methodology): “ANNs have been applied to several fields of science (see for example Gardner and Dorling, 1998; Dayhoff and DeLeo, 2001) and several applications exist also in the field of ocean and coastal engineering.”
Artificial neural networks in medical diagnosis (2013, background): “Some types of neural networks are suitable for solving perceptual problems while others are more adapted for data modeling and functional approximation (Dayhoff and Deleo 2001).”