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 711–720 of 3569 papers

TitleStatusHype
Automatic Self-supervised Learning for Social Recommendations—0
Black-box Prompt Tuning with Subspace Learning—0
Adaptive Robust Model Predictive Control via Uncertainty Cancellation—0
A Comprehensive Sustainable Framework for Machine Learning and Artificial Intelligence—0
Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks—0
A Meta Understanding of Meta-Learning—0
Efficient Gradient Approximation Method for Constrained Bilevel Optimization—0
Efficient In-Context Medical Segmentation with Meta-driven Visual Prompt Selection—0
A Meta-transfer Learning framework for Visually Grounded Compositional Concept Learning—0
BI-MAML: Balanced Incremental Approach for Meta Learning—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