SOTAVerified

Meta-Learning

Meta-learning is a methodology considered with "learning to learn" machine learning algorithms.

( Image credit: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks )

Papers

Showing 19211930 of 3569 papers

TitleStatusHype
On the cross-lingual transferability of multilingual prototypical models across NLU tasks0
On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning0
A Survey on Stability of Learning with Limited Labelled Data and its Sensitivity to the Effects of Randomness0
On the Efficiency of Integrating Self-supervised Learning and Meta-learning for User-defined Few-shot Keyword Spotting0
On the Energy and Communication Efficiency Tradeoffs in Federated and Multi-Task Learning0
On the ERM Principle in Meta-Learning0
On-the-Fly Adaptation of Source Code Models using Meta-Learning0
On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classification0
On the Generalization Error of Meta Learning for the Gibbs Algorithm0
On the Generalization of Neural Combinatorial Optimization Heuristics0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified