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 1551–1560 of 3569 papers

TitleStatusHype
Principled Acceleration of Iterative Numerical Methods Using Machine Learning—0
Function Contrastive Learning of Transferable Meta-Representations—0
Function Class Learning with Genetic Programming: Towards Explainable Meta Learning for Tumor Growth Functionals—0
Learning To Learn Around A Common Mean—0
Functionally Regionalized Knowledge Transfer for Low-resource Drug Discovery—0
Learning a Meta-Solver for Syntax-Guided Program Synthesis—0
Learning a Terrain- and Robot-Aware Dynamics Model for Autonomous Mobile Robot Navigation—0
Learning Attentive Meta-Transfer—0
Fully Online Meta-Learning Without Task Boundaries—0
Contextual HyperNetworks for Novel Feature Adaptation—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