What is recorded here?
Repeatedly sampled a task, trained on it and moved a shared parameter initialization toward the resulting weights, learning an initialization for fast adaptation without differentiating through the inner optimization.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
MAML and earlier meta-learning are inherited. Benchmark adaptation does not establish arbitrary-task generalization.
Read the original work
- Reptile: a Scalable Metalearning Algorithm
- Nichol and Schulman: first-order initialization meta-learning — original project
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
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People and systems
Alex Nichol; John Schulman
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
Nichol and Schulman: first-order initialization meta-learning
Repeatedly sampled a task, trained on it and moved a shared parameter initialization toward the resulting weights, learning an initialization for fast adaptation without differentiating through the inner optimization.
Scientific scope
MAML and earlier meta-learning are inherited. Benchmark adaptation does not establish arbitrary-task generalization.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Alex Nichol; John Schulman
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.