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 321–330 of 3569 papers

TitleStatusHype
Fast Adaptation with Kernel and Gradient based Meta Leaning—0
Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-OptimizationCode2
First, Learn What You Don't Know: Active Information Gathering for Driving at the Limits of Handling—0
Progressive Safeguards for Safe and Model-Agnostic Reinforcement Learning—0
Theoretical Investigations and Practical Enhancements on Tail Task Risk Minimization in Meta LearningCode0
Hyperparameter Optimization in Machine Learning—0
Meta-Learning Adaptable Foundation Models—0
Meta-Learning for Speeding Up Large Model Inference in Decentralized Environments—0
Leveraging Auxiliary Task Relevance for Enhanced Bearing Fault Diagnosis through Curriculum Meta-learning—0
Few-shot Open Relation Extraction with Gaussian Prototype and Adaptive Margin—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