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 16011610 of 3569 papers

TitleStatusHype
MetaAudio: A Few-Shot Audio Classification BenchmarkCode1
Model Based Meta Learning of Critics for Policy Gradients0
Towards Explainable Meta-Learning for DDoS Detection0
Disentangling Abstraction from Statistical Pattern Matching in Human and Machine LearningCode0
Context-aware Visual Tracking with Joint Meta-updating0
Meta-Learning Approaches for a One-Shot Collective-Decision Aggregation: Correctly Choosing how to Choose Correctly0
A Dynamic Meta-Learning Model for Time-Sensitive Cold-Start Recommendations0
AutoProtoNet: Interpretability for Prototypical NetworksCode0
Scalable Semi-Modular Inference with Variational Meta-PosteriorsCode0
Diverse Preference Augmentation with Multiple Domains for Cold-start RecommendationsCode0
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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