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 1341–1350 of 3569 papers

TitleStatusHype
Beyond Bayes-optimality: meta-learning what you know you don't know—0
The Curse of Unrolling: Rate of Differentiating Through Optimization—0
Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-LearningCode0
TaskMix: Data Augmentation for Meta-Learning of Spoken Intent Understanding—0
Meta-Learning a Cross-lingual Manifold for Semantic ParsingCode0
Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated LearningCode0
Graph Neural Network Expressivity and Meta-Learning for Molecular Property Regression—0
Defending against Poisoning Backdoor Attacks on Federated Meta-learning—0
Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-LearningCode1
MetaPrompting: Learning to Learn Better PromptsCode1
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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