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 581590 of 3569 papers

TitleStatusHype
Learning Compositional Rules via Neural Program SynthesisCode1
Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual LearningCode1
Meta-learning curiosity algorithmsCode1
Online Meta-Critic Learning for Off-Policy Actor-Critic MethodsCode1
Fast Online Adaptation in Robotics through Meta-Learning Embeddings of Simulated PriorsCode1
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot LearningCode1
TaskNorm: Rethinking Batch Normalization for Meta-LearningCode1
Zero-Shot Cross-Lingual Transfer with Meta LearningCode1
End-to-End Fast Training of Communication Links Without a Channel Model via Online Meta-LearningCode1
Meta-Transfer Learning for Zero-Shot Super-ResolutionCode1
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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