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 361–370 of 3569 papers

TitleStatusHype
Improving Arabic Multi-Label Emotion Classification using Stacked Embeddings and Hybrid Loss FunctionCode0
Should Cross-Lingual AMR Parsing go Meta? An Empirical Assessment of Meta-Learning and Joint Learning AMR ParsingCode0
Zebra: In-Context and Generative Pretraining for Solving Parametric PDEs—0
MetaOOD: Automatic Selection of OOD Detection Models—0
Unsupervised Meta-Learning via Dynamic Head and Heterogeneous Task Construction for Few-Shot ClassificationCode0
Learning to learn ecosystems from limited data -- a meta-learning approachCode0
Meta-TTT: A Meta-learning Minimax Framework For Test-Time Training—0
Reevaluating Meta-Learning Optimization Algorithms Through Contextual Self-ModulationCode0
Reducing Variance in Meta-Learning via Laplace Approximation for Regression Tasks—0
Recovering Time-Varying Networks From Single-Cell Data—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