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 3121–3130 of 3569 papers

TitleStatusHype
Improving Memory Efficiency for Training KANs via Meta LearningCode0
Meta-GNN: On Few-shot Node Classification in Graph Meta-learningCode0
Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-TrainingCode0
Improving Generalization in Meta-Learning via Meta-Gradient AugmentationCode0
Theoretical Convergence of Multi-Step Model-Agnostic Meta-LearningCode0
Meta-Gradient Reinforcement LearningCode0
Meta-Graph: Few Shot Link Prediction via Meta LearningCode0
A Cost-Sensitive Meta-Learning Strategy for Fair Provider Exposure in RecommendationCode0
MetaGreen: Meta-Learning Inspired Transformer Selection for Green Semantic CommunicationCode0
Bottom-Up Meta-Policy SearchCode0
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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