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 3301–3310 of 3569 papers

TitleStatusHype
Learning What and Where to TransferCode0
Meta-Learning for Low-resource Natural Language Generation in Task-oriented Dialogue Systems—0
Hierarchically Structured Meta-learningCode0
Few-Shot Viewpoint Estimation—0
Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category GraphCode0
Meta-learning of Sequential Strategies—0
Data-Efficient Mutual Information Neural Estimator—0
MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health RecordsCode0
Interpretable Automated Machine Learning in Maana(TM) Knowledge Platform—0
Few-Shot Adaptive Gaze EstimationCode0
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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