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 1191–1200 of 3569 papers

TitleStatusHype
Data-Efficient Cross-Lingual Transfer with Language-Specific Subnetworks—0
A supervised generative optimization approach for tabular data—0
Data-Efficient and Robust Task Selection for Meta-Learning—0
A Classification View on Meta Learning Bandits—0
Higher-Order Generalization Bounds: Learning Deep Probabilistic Programs via PAC-Bayes Objectives—0
How Fine-Tuning Allows for Effective Meta-Learning—0
How to distribute data across tasks for meta-learning?—0
A Hybrid Model For Grammatical Error Correction—0
A study on Ensemble Learning for Time Series Forecasting and the need for Meta-Learning—0
A Student-Teacher Architecture for Dialog Domain Adaptation under the Meta-Learning Setting—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