Stochastic Gradient Descent
Stochastic Gradient Descent is an optimization algorithm that updates the parameters of a model iteratively based on the gradient of the loss function with respect to a randomly selected subset of data.
Stochastic Gradient Descent is an optimization algorithm that updates the parameters of a model iteratively based on the gradient of the loss function with respect to a randomly selected subset of data.
Conjugate Gradient is an iterative method for solving large systems of linear equations, particularly those that are symmetric and positive-definite.
A structured approach to optimization that allows for efficient computation and solution finding, particularly in complex problems.
A mathematical formulation that provides an exact solution to a problem without the need for iterative approximation.
BlockCIR is a groupwise extension of ExCIR that evaluates sets of correlated features as a single entity to prevent double-counting.
Robust centering involves subtracting a robust estimate, such as the median or mid-mean, from features and outputs to enhance stability in feature attribution.
ExCIR is a correlation-aware attribution score that quantifies the impact of feature co-movement on model outputs while reducing computational costs.
A method applied after data generation to refine or select the generated samples based on certain criteria, improving their statistical properties.
An adaptive lower bound is a dynamic threshold that adjusts to avoid vacuous acceptance regions during optimization.
A memory mechanism that restricts comparisons to a fixed-size subset of past evaluations to improve computational efficiency.