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 951960 of 3569 papers

TitleStatusHype
Asynchronous Distributed Bilevel OptimizationCode0
Learning How to Demodulate from Few Pilots via Meta-LearningCode0
Learning to Design RNACode0
Learning to Propagate Labels: Transductive Propagation Network for Few-shot LearningCode0
Deceptive Fairness Attacks on Graphs via Meta LearningCode0
Latent Representation Learning of Multi-scale Thermophysics: Application to Dynamics in Shocked Porous Energetic MaterialCode0
Latent Task-Specific Graph Network SimulatorsCode0
Evaluating recommender systems for AI-driven biomedical informaticsCode0
Latent Bottlenecked Attentive Neural ProcessesCode0
Adaptive Domain-Specific Normalization for Generalizable Person Re-IdentificationCode0
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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