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 111–120 of 3569 papers

TitleStatusHype
The Athenian Academy: A Seven-Layer Architecture Model for Multi-Agent Systems—0
Manifold meta-learning for reduced-complexity neural system identificationCode0
Meta-learning For Few-Shot Time Series Crop Type Classification: A Benchmark On The EuroCropsML DatasetCode0
Adapting to the Unknown: Robust Meta-Learning for Zero-Shot Financial Time Series Forecasting—0
Combining Forecasts using Meta-Learning: A Comparative Study for Complex Seasonality—0
Exploiting Meta-Learning-based Poisoning Attacks for Graph Link PredictionCode0
An experimental survey and Perspective View on Meta-Learning for Automated Algorithms Selection and Parametrization—0
Meta-Continual Learning of Neural Fields—0
Federated Neural Architecture Search with Model-Agnostic Meta Learning—0
The challenge of uncertainty quantification of large language models in medicine—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