Meta-Learning Suite (MAML/FOMAML/Reptile + MAML++ Enhancements)
CommunityMeta-learning testbed for rapid adaptation.
Authorsovr610
Version1.0.0
Installs0
System Documentation
What problem does it solve?
The Meta-Learning Suite standardizes the learn-to-adapt pipeline, providing a self-contained framework for designing, evaluating, and reproducing gradient-based meta-learning algorithms (MAML, FOMAML, Reptile) with MAML++ enhancements.
Core Features & Use Cases
- Supports second-order and first-order inner loops through pluggable backends (torch.func, higher, or custom SGD).
- Implements MAML++ enhancements: per-layer per-step learning rates (LSLR), multi-step loss (MSL), derivative-order annealing, and per-step batch-norm handling.
- Deterministic episodic sampling, comprehensive checkpointing, and a consistent evaluation interface for research and prototyping.
Quick Start
Define a model, configure a meta-algorithm (MAML or FOMAML), and run a training loop to observe the inner-loop adaptations and outer meta-gradients.
Dependency Matrix
Required Modules
torchpytest
Components
scriptsreferencesassets
💻 Claude Code Installation
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Please help me install this Skill: Name: Meta-Learning Suite (MAML/FOMAML/Reptile + MAML++ Enhancements) Download link: https://github.com/sovr610/refffiy/archive/main.zip#meta-learning-suite-maml-fomaml-reptile-maml-enhancements Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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