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 131140 of 3569 papers

TitleStatusHype
You Can Backdoor Personalized Federated LearningCode1
Consistency-guided Meta-Learning for Bootstrapping Semi-Supervised Medical Image SegmentationCode1
Towards Task Sampler Learning for Meta-LearningCode1
Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured LearningCode1
Generative Meta-Learning Robust Quality-Diversity PortfolioCode1
OntoChatGPT Information System: Ontology-Driven Structured Prompts for ChatGPT Meta-LearningCode1
First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-OffsCode1
Improving Language Plasticity via Pretraining with Active ForgettingCode1
Graph Sampling-based Meta-Learning for Molecular Property PredictionCode1
Unsupervised Episode Generation for Graph Meta-learningCode1
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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