Meta-Learning System

A Meta-Learning System is a machine learning system that systematically and automatically solves a metalearning task by implementing a metalearning algorithm to generalize across tasks.

• AKA: Meta-Learning Framework, Learning-to-Learn Engine, Meta-Level Learner.
• Context:
  • It can utilize algorithms, methods, techniques, and models:
    • Model-Agnostic Meta-Learning, for rapid adaptation to new tasks.
    • Prototypical Networks, to classify based on learned metric spaces.
    • Memory-Augmented Neural Networks, for using external memory in rapid learning.
    • First-order MAML variants, to improve computational scalability.
  • It can learn representations or policies that adapt quickly to unseen task distributions.
  • It can include inner-loop base learners and outer-loop meta-learners.
  • It can be used in real-world settings such as robotics, clinical diagnosis, or AutoML pipelines.
  • It can track and reuse knowledge across episodes or tasks for continual performance.
  • ...
• Example(s):
  • LEO system, which operates in a latent embedding space for meta-learning.
  • Reptile-based system, that approximates gradient-based learning without second-order derivatives.
  • MetaNAS system, that uses metalearning to design neural architectures.
  • ...
• Counter-Example(s):
  • Standard supervised learning system, which does not generalize across tasks.
  • Transfer learning system, which transfers knowledge without learning the learning process.
  • ...
• See: Metalearning Task, Metalearning Algorithm, Few-shot learning system, AutoML system.

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