Cross-entropy method (CEM) for planning
The Cross-entropy method (CEM) for planning is a statistical technique used in optimization and decision-making processes, particularly in complex systems.
optimization
DARE (drop and re-scale) for model merging
DARE (drop and re-scale) is a technique used in model merging to efficiently combine multiple machine learning models, enhancing performance and resource management.
Evolutionary computation
Evolutionary computation is a subset of artificial intelligence that uses mechanisms inspired by biological evolution to solve complex problems.
Loss function
A loss function is a mathematical function used in optimization and machine learning to measure the difference between predicted values and actual values. It guides algorithms in minimizing errors during model training.
Genetic algorithm
Genetic algorithms are optimization techniques inspired by natural selection, used in various fields to solve complex problems.
Ant colony optimization
Ant colony optimization is a computational algorithm inspired by the foraging behavior of ants, used for solving complex optimization problems.
Stochastic weight averaging (SWA)
Stochastic weight averaging (SWA) is an optimization technique used in training deep neural networks. It involves averaging multiple sets of weights collected at different points during the training process to improve generalization and model performance.
Stochastic gradient descent
Stochastic gradient descent is an optimization algorithm used in machine learning and statistical modeling to minimize loss functions by iteratively updating parameters using random subsets of data.
Swarm intelligence
Swarm intelligence is a concept in artificial intelligence that mimics the collective behavior of decentralized systems, commonly observed in nature.
Gradient descent
Gradient descent is an optimization algorithm used to minimize functions by iteratively moving toward the steepest descent direction. It is widely employed in machine learning and numerical optimization to find parameter values that minimize a cost or loss function.