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💬 프롬프트 라이브러리 📖 AI 용어 사전 🔗 유용한 링크

AI 용어집

인공지능 완전 사전

162
카테고리
2,032
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23,060
용어
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용어

Gradient-Based Hyperparameter Optimization

Optimization method that uses gradients to adjust hyperparameters continuously, enabling faster convergence than traditional search methods.

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Hypergradient

Gradient of the loss function with respect to hyperparameters, computed using automatic differentiation through the model parameter optimization process.

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Bilevel Optimization

Hierarchical optimization problem where hyperparameters (upper level) optimize model performance after parameters (lower level) have converged.

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Implicit Differentiation

Technique for computing gradients without explicit backpropagation, using the implicit function theorem for optimization equilibrium points.

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Hyperparameter Sensitivity Analysis

Quantitative study of the influence of hyperparameter variations on model performance, using gradient information to identify critical parameters.

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Differentiable Programming

Programming paradigm where programs are fully differentiable, enabling gradient optimization of all computation aspects including hyperparameters.

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Unrolled Optimization

Technique where parameter optimization steps are explicitly unrolled in the computation graph to allow backpropagation through the optimization process.

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Hyperparameter Differentiation

Mathematical process of computing partial derivatives of the objective function with respect to hyperparameters, often through the reverse chain rule.

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Gradient Descent for Hyperparameters

Application of the gradient descent algorithm directly to the hyperparameter space, using continuous approximations for discrete parameters.

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Neural Architecture Optimization

Subfield of NAS using gradient-based methods to discover and continuously optimize neural network architectures.

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Hyperparameter Regularization

Technique adding penalty terms on hyperparameters in the objective function to stabilize their gradient-based optimization and prevent overfitting.

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Differentiable Augmentation Search

Method optimizing data augmentation policies through gradient, treating augmentation choices as continuous parameters in probability space.

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