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 2711–2720 of 3569 papers

TitleStatusHype
Meta-Learning for Unsupervised Outlier Detection with Optimal Transport—0
Meta-Learning for Variational Inference—0
Meta-Learning Divergences of Variational Inference—0
Meta-learning For Vision-and-language Cross-lingual Transfer—0
Meta learning Framework for Automated Driving—0
Meta-learning from Learning Curves Challenge: Lessons learned from the First Round and Design of the Second Round—0
Meta-Learning from Learning Curves for Budget-Limited Algorithm Selection—0
Metalearning generalizable dynamics from trajectories—0
Meta-Learning GNN Initializations for Low-Resource Molecular Property Prediction—0
Meta-Learning Guarantees for Online Receding Horizon Learning Control—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