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 2251–2260 of 3569 papers

TitleStatusHype
Adaptive Optimization Algorithms for Machine Learning—0
Adaptive Personlization in Federated Learning for Highly Non-i.i.d. Data—0
Adaptive Physics-informed Neural Networks: A Survey—0
Adaptive Reinforcement Learning through Evolving Self-Modifying Neural Networks—0
Adaptive Resource Allocation for Virtualized Base Stations in O-RAN with Online Learning—0
Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift—0
Adaptive Robust Model Predictive Control via Uncertainty Cancellation—0
Automatic Self-supervised Learning for Social Recommendations—0
Adaptive Self-training for Few-shot Neural Sequence Labeling—0
Adaptive Self-training for Neural Sequence Labeling with Few Labels—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