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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1501–1510 of 3569 papers

TitleStatusHype
MetaTPTrans: A Meta Learning Approach for Multilingual Code Representation LearningCode1
Faster Optimization-Based Meta-Learning Adaptation Phase—0
A General framework for PAC-Bayes Bounds for Meta-Learning—0
Lightweight Conditional Model Extrapolation for Streaming Data under Class-Prior ShiftCode0
Learning to generate imaginary tasks for improving generalization in meta-learning—0
Deep Meta-learning in Recommendation Systems: A Survey—0
Data-Efficient Brain Connectome Analysis via Multi-Task Meta-LearningCode1
Comprehensive Fair Meta-learned Recommender SystemCode0
Neural Diffusion ProcessesCode1
Sharp-MAML: Sharpness-Aware Model-Agnostic Meta LearningCode1
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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