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 931940 of 3569 papers

TitleStatusHype
Deep Learning Theory Review: An Optimal Control and Dynamical Systems PerspectiveCode0
Learning advisor networks for noisy image classificationCode0
learn2learn: A Library for Meta-Learning ResearchCode0
Leaping Through Time with Gradient-based Adaptation for RecommendationCode0
Deep Compressed SensingCode0
Learning an Explicit Hyperparameter Prediction Function Conditioned on TasksCode0
Latent Representation Learning of Multi-scale Thermophysics: Application to Dynamics in Shocked Porous Energetic MaterialCode0
Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal ResourcesCode0
Latent-Optimized Adversarial Neural Transfer for Sarcasm DetectionCode0
Latent Task-Specific Graph Network SimulatorsCode0
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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