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 3471–3480 of 3569 papers

TitleStatusHype
Meta-Learning for Few-Shot Named Entity Recognition—0
Meta-learning for Few-shot Natural Language Processing: A Survey—0
Meta-Learning for Few-Shot NMT Adaptation—0
Meta-Learning for Few-Shot Time Series Classification—0
Meta-learning for heterogeneous treatment effect estimation with closed-form solvers—0
Meta Learning for High-dimensional Ising Model Selection Using _1-regularized Logistic Regression—0
Meta-Learning for improving rare word recognition in end-to-end ASR—0
Meta-Learning for Koopman Spectral Analysis with Short Time-series—0
Meta-Learning for Low-resource Natural Language Generation in Task-oriented Dialogue Systems—0
Meta-Learning for Low-Resource Neural Machine Translation—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