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

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Showing 411–420 of 3569 papers

TitleStatusHype
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel LearningCode1
A Meta-Learning Approach for Graph Representation Learning in Multi-Task SettingsCode1
MC-BERT: Efficient Language Pre-Training via a Meta ControllerCode1
MedFuncta: Modality-Agnostic Representations Based on Efficient Neural FieldsCode1
A Broader Study of Cross-Domain Few-Shot LearningCode1
Can Learned Optimization Make Reinforcement Learning Less Difficult?Code1
A Meta-Learning Approach for Training Explainable Graph Neural NetworksCode1
Meta Adversarial Training against Universal PatchesCode1
Copolymer Informatics with Multi-Task Deep Neural NetworksCode1
Covariate Distribution Aware Meta-learningCode1
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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