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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2741–2750 of 3569 papers

TitleStatusHype
Meta-Learning Mini-Batch Risk Functionals—0
Meta Learning MPC using Finite-Dimensional Gaussian Process Approximations—0
Meta-Learning Multi-task Communication—0
Meta-Learning Neural Bloom Filters—0
Meta-Learning Neural Mechanisms rather than Bayesian Priors—0
Meta-Learning Neural Procedural Biases—0
Meta Learning not to Learn: Robustly Informing Meta-Learning under Nuisance-Varying Families—0
Meta learning of bounds on the Bayes classifier error—0
Meta-learning of data-driven controllers with automatic model reference tuning: theory and experimental case study—0
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks—0
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All datasetsML10MT50ML45OMNIGLOT - 1-Shot, 20-way

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8—Unverified
2MZMeta-train success rate97.6—Unverified
3MAMLMeta-test success rate36—Unverified
4RL^2Meta-test success rate10—Unverified
5DnCMeta-test success rate5.4—Unverified
6PEARLMeta-test success rate0—Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60—Unverified
2Multi-task multi-head SACAverage Success Rate35.85—Unverified
3DisCorAverage Success Rate26—Unverified
4NDPAverage Success Rate11—Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5—Unverified
2MZMeta-test success rate (zero-shot)17.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75—Unverified