AI-woordenlijst
Het complete woordenboek van kunstmatige intelligentie
Gradient-Based Hyperparameter Optimization
Optimization method that uses gradients to adjust hyperparameters continuously, enabling faster convergence than traditional search methods.
Hypergradient
Gradient of the loss function with respect to hyperparameters, computed using automatic differentiation through the model parameter optimization process.
Bilevel Optimization
Hierarchical optimization problem where hyperparameters (upper level) optimize model performance after parameters (lower level) have converged.
Implicit Differentiation
Technique for computing gradients without explicit backpropagation, using the implicit function theorem for optimization equilibrium points.
Hyperparameter Sensitivity Analysis
Quantitative study of the influence of hyperparameter variations on model performance, using gradient information to identify critical parameters.
Differentiable Programming
Programming paradigm where programs are fully differentiable, enabling gradient optimization of all computation aspects including hyperparameters.
Unrolled Optimization
Technique where parameter optimization steps are explicitly unrolled in the computation graph to allow backpropagation through the optimization process.
Hyperparameter Differentiation
Mathematical process of computing partial derivatives of the objective function with respect to hyperparameters, often through the reverse chain rule.
Gradient Descent for Hyperparameters
Application of the gradient descent algorithm directly to the hyperparameter space, using continuous approximations for discrete parameters.
Neural Architecture Optimization
Subfield of NAS using gradient-based methods to discover and continuously optimize neural network architectures.
Hyperparameter Regularization
Technique adding penalty terms on hyperparameters in the objective function to stabilize their gradient-based optimization and prevent overfitting.
Differentiable Augmentation Search
Method optimizing data augmentation policies through gradient, treating augmentation choices as continuous parameters in probability space.