Residual-based Adaptive Refinement is a strategy used to improve the accuracy and efficiency of by intelligently selecting training data points.
: While adaptive sampling approaches often rank and select points based on residual errors, RAR specifically chooses the "top k" largest residual points without necessarily differentiating between them further. 13988 rar
The search result for "13988 rar" primarily refers to a scientific paper on arXiv:2112.13988 , which discusses a machine learning technique called . Review of RAR in Machine Learning Residual-based Adaptive Refinement is a strategy used to
: Other sophisticated adaptive strategies can become computationally expensive as the number of training points accumulates over time. RAR is often viewed as a more balanced fit because it can refine the model without letting the training set grow uncontrollably. Strengths : 13988 rar