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

Showing 25912600 of 3569 papers

TitleStatusHype
Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes0
Convergence of Gradient-based MAML in LQR0
Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters0
Convergence Properties of Stochastic Hypergradients0
MetaCVR: Conversion Rate Prediction via Meta Learning in Small-Scale Recommendation Scenarios0
Convolutional Neural Networks Can (Meta-)Learn the Same-Different Relation0
Convolutional Neural Processes for Inpainting Satellite Images0
Coordinated Control of Deformation and Flight for Morphing Aircraft via Meta-Learning and Coupled State-Dependent Riccati Equations0
Correction Networks: Meta-Learning for Zero-Shot Learning0
CosmosDSR -- a methodology for automated detection and tracking of orbital debris using the Unscented Kalman Filter0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified