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 1951–1960 of 3569 papers

TitleStatusHype
Ornstein-Uhlenbeck Adaptation as a Mechanism for Learning in Brains and Machines—0
OSSEM: one-shot speaker adaptive speech enhancement using meta learning—0
Out-of-Domain Generalization from a Single Source: An Uncertainty Quantification Approach—0
Overlap-aware meta-learning attention to enhance hypergraph neural networks for node classification—0
Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice—0
PAC-Bayes meta-learning with implicit task-specific posteriors—0
PAC Prediction Sets for Meta-Learning—0
Learn2Weight: Parameter Adaptation against Similar-domain Adversarial Attacks—0
ParsRec: A Novel Meta-Learning Approach to Recommending Bibliographic Reference Parsers—0
ParsRec: Meta-Learning Recommendations for Bibliographic Reference Parsing—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