Welcome to meta-blocks’s documentation!¶
Deployment & Documentation & Stats
Meta-Blocks is a modular toolbox for research, experimentation, and reproducible benchmarking of learning-to-learn algorithms.
The toolbox provides flexible APIs for working with MetaDatasets, TaskDistributions, and MetaLearners (see the figure below).
The APIs make it easy to implement a variety of meta-learning algorithms, run them on well-established benchmarks,
or add your own meta-learning problems to the suite and benchmark algorithms on them.
Meta-Blocks package comes with:
Flexible APIs, detailed documentation, and multiple examples.
Popular models and algorithms such as MAML [FAL17], Reptile [NAS18], Protonets [SSZ17].
Supervised and unsupervised meta-learning setups compatible with all algorithms.
Customizable modules and utility functions for quick prototyping on new meta-learning algorithms.
Key Links and Resources:
Getting Started
Documentation
References
- FAL17
Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In ICML, 1126–1135. JMLR. org, 2017.
- NAS18
Alex Nichol, Joshua Achiam, and John Schulman. On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999, 2018.
- SSZ17
Jake Snell, Kevin Swersky, and Richard Zemel. Prototypical networks for few-shot learning. In NeurIPS, 4077–4087. 2017.