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 641650 of 3569 papers

TitleStatusHype
Meta-Learning with Latent Embedding OptimizationCode1
Modular meta-learningCode1
Bayesian Model-Agnostic Meta-LearningCode1
Meta-learning with differentiable closed-form solversCode1
Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement LearningCode1
Learning to Reweight Examples for Robust Deep LearningCode1
On First-Order Meta-Learning AlgorithmsCode1
Meta-Learning for Semi-Supervised Few-Shot ClassificationCode1
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel LearningCode1
Learning to Compare: Relation Network for Few-Shot LearningCode1
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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