Namespace AiDotNet.MetaLearning.Options
Classes
- ANILOptions<T, TInput, TOutput>
Configuration options for Almost No Inner Loop (ANIL) algorithm.
- BOILOptions<T, TInput, TOutput>
Configuration options for Body Only Inner Loop (BOIL) algorithm.
- CNAPOptions<T, TInput, TOutput>
Configuration options for the Conditional Neural Adaptive Processes (CNAP) algorithm.
- GNNMetaOptions<T, TInput, TOutput>
Configuration options for the Graph Neural Network Meta-learning algorithm.
- LEOOptions<T, TInput, TOutput>
Configuration options for Latent Embedding Optimization (LEO) algorithm.
- MAMLOptions<T, TInput, TOutput>
Configuration options for MAML (Model-Agnostic Meta-Learning) algorithm.
- MANNOptions<T, TInput, TOutput>
Configuration options for Memory-Augmented Neural Networks (MANN) algorithm.
- MatchingNetworksOptions<T, TInput, TOutput>
Configuration options for Matching Networks algorithm.
- MetaOptNetOptions<T, TInput, TOutput>
Configuration options for Meta-learning with Differentiable Convex Optimization (MetaOptNet) algorithm.
- MetaSGDOptions<T, TInput, TOutput>
Configuration options for the Meta-SGD (Meta Stochastic Gradient Descent) algorithm.
- NTMOptions<T, TInput, TOutput>
Configuration options for Neural Turing Machine (NTM) algorithm.
- ProtoNetsOptions<T, TInput, TOutput>
Configuration options for Prototypical Networks (ProtoNets) algorithm.
- RelationNetworkOptions<T, TInput, TOutput>
Configuration options for Relation Networks algorithm.
- ReptileOptions<T, TInput, TOutput>
Configuration options for the Reptile meta-learning algorithm.
- SEALOptions<T, TInput, TOutput>
Configuration options for the SEAL (Sample-Efficient Adaptive Learning) meta-learning algorithm.
- TADAMOptions<T, TInput, TOutput>
Configuration options for Task-Dependent Adaptive Metric (TADAM) algorithm.
- iMAMLOptions<T, TInput, TOutput>
Configuration options for iMAML (Implicit Model-Agnostic Meta-Learning) algorithm.
Enums
- FastWeightApplicationMode
Specifies how fast weights are applied to modify the base model.
- GNNAggregationType
Specifies how nodes are aggregated to form graph-level representations.
- MatchingNetworksAttentionFunction
Attention function types for Matching Networks.
- MetaSGDLearningRateInitialization
Learning rate initialization strategies for Meta-SGD.
- MetaSGDLearningRateScheduleType
Learning rate schedule types for Meta-SGD meta-training.
- MetaSGDUpdateRuleType
Update rule types for Meta-SGD per-parameter optimization.
- NTMControllerType
Controller type for Neural Turing Machine.
- NTMMemoryInitialization
Memory initialization strategies for NTM.
- ProtoNetsDistanceFunction
Distance functions supported by Prototypical Networks for measuring similarity between embeddings.
- SEALAdaptiveLearningRateMode
Specifies the mode for computing adaptive learning rates in SEAL.
- TaskSimilarityMetric
Specifies how task similarity is computed for building the task graph.