Namespace AiDotNet.MetaLearning.Algorithms
Classes
- ANILAlgorithm<T, TInput, TOutput>
Implementation of Almost No Inner Loop (ANIL) meta-learning algorithm.
- BOILAlgorithm<T, TInput, TOutput>
Implementation of Body Only Inner Loop (BOIL) meta-learning algorithm.
- CNAPAlgorithm<T, TInput, TOutput>
Implementation of Conditional Neural Adaptive Processes (CNAP) for meta-learning.
- ExternalMemory<T>
External memory matrix for MANN.
- GNNMetaAlgorithm<T, TInput, TOutput>
Implementation of Graph Neural Network-based Meta-learning.
- LEOAlgorithm<T, TInput, TOutput>
Implementation of Latent Embedding Optimization (LEO) meta-learning algorithm.
- LSTMNTMController<T, TInput, TOutput>
LSTM-based NTM controller implementation with learnable parameters.
- MAMLAlgorithm<T, TInput, TOutput>
Implementation of the MAML (Model-Agnostic Meta-Learning) algorithm.
- MANNAlgorithm<T, TInput, TOutput>
Implementation of Memory-Augmented Neural Networks (MANN) for meta-learning.
- MANNMemoryStatistics
Memory statistics tracking for MANN.
- MANNModel<T, TInput, TOutput>
MANN model for inference with external memory.
- MLPNTMController<T, TInput, TOutput>
MLP-based NTM controller implementation with learnable parameters.
- MatchingNetworksAlgorithm<T, TInput, TOutput>
Implementation of Matching Networks for few-shot learning.
- MatchingNetworksModel<T, TInput, TOutput>
Matching Networks model for inference.
- MetaOptNetAlgorithm<T, TInput, TOutput>
Implementation of Meta-learning with Differentiable Convex Optimization (MetaOptNet) algorithm.
- MetaSGDAdaptedModel<T, TInput, TOutput>
Wrapper model for Meta-SGD adapted models that includes the per-parameter optimizer.
- MetaSGDAlgorithm<T, TInput, TOutput>
Implementation of Meta-SGD (Meta Stochastic Gradient Descent) algorithm.
- NTMAlgorithm<T, TInput, TOutput>
Implementation of Neural Turing Machine (NTM) for meta-learning.
- NTMMemory<T>
External memory matrix for Neural Turing Machine.
- NTMModel<T, TInput, TOutput>
NTM model for inference with persistent memory.
- NTMReadHead<T>
NTM read head for content-based addressing.
- NTMWriteHead<T>
NTM write head for content-based addressing.
- PerParameterOptimizer<T, TInput, TOutput>
Per-parameter optimizer for Meta-SGD that learns individual optimization coefficients.
- ProtoNetsAlgorithm<T, TInput, TOutput>
Implementation of Prototypical Networks (ProtoNets) algorithm for few-shot learning.
- PrototypicalModel<T, TInput, TOutput>
Prototypical model for few-shot classification.
- RelationNetworkAlgorithm<T, TInput, TOutput>
Implementation of Relation Networks algorithm for few-shot learning.
- ReptileAlgorithm<T, TInput, TOutput>
Implementation of the Reptile meta-learning algorithm.
- SEALAlgorithm<T, TInput, TOutput>
Implementation of the SEAL (Sample-Efficient Adaptive Learning) meta-learning algorithm.
- TADAMAlgorithm<T, TInput, TOutput>
Implementation of Task-Dependent Adaptive Metric (TADAM) algorithm for few-shot learning.
- iMAMLAlgorithm<T, TInput, TOutput>
Implementation of the iMAML (Implicit Model-Agnostic Meta-Learning) algorithm.
Interfaces
- INTMController<T>
Interface for NTM controller.