Module few_shot_learning_enhancement

Module few_shot_learning_enhancement 

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Few-Shot Learning Enhancement for Optimization

This module implements few-shot learning capabilities that allow optimizers to quickly adapt to new optimization problems with minimal training data. The system leverages meta-learning and rapid adaptation techniques.

Structs§

AdaptationStrategySelector
Strategy selector for adaptation methods
ContextEncoder
Context encoder for task understanding
ConvLayer
Convolutional layer
ConvergenceCriteria
Convergence criteria
DenseLayer
Dense layer
Episode
Episode in episodic memory
ExperienceMemory
Experience memory for few-shot learning
FastAdaptationMechanism
Fast adaptation mechanism
FeatureAttention
Feature attention mechanism
FeatureExtractor
Feature extractor for optimization problems
FewShotAdaptationStats
Few-shot adaptation statistics
FewShotLearningOptimizer
Few-Shot Learning Optimizer with Rapid Adaptation
GradientBasedAdapter
Gradient-based adaptation
InnerLoopOptimizer
Inner loop optimizer for MAML
LSTMCell
LSTM cell
MAMLAdapter
Model-Agnostic Meta-Learning adapter
MemoryNetwork
Memory network for storing optimization patterns
MetaLearnerNetwork
Meta-learner network for few-shot optimization
MetaOptimizer
Meta-optimizer for MAML
OptimizationStrategy
Generated optimization strategy
OptimizationTrajectory
Optimization trajectory
ParameterGenerator
Parameter generator for optimization strategies
ProblemSimilarityMatcher
Problem similarity matcher
PrototypeBasedAdapter
Prototype-based adaptation
QueryExample
Query example for evaluation
SupportExample
Support example for few-shot learning
TaskContext
Task context for current optimization
UpdateNetwork
Update network for parameter adaptation

Enums§

AdaptationStrategy
Types of adaptation strategies
DirectionComputation
Direction computation methods
DistanceMetric
Distance metrics for prototype matching
StepSizeSchedule
Step size schedule types

Functions§

few_shot_optimize
Convenience function for few-shot learning optimization
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