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 1291–1300 of 3569 papers

TitleStatusHype
GradMix: Multi-source Transfer across Domains and Tasks—0
GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization—0
Grammatical Error Correction Using Feature Selection and Confidence Tuning—0
A Nested Bi-level Optimization Framework for Robust Few Shot Learning—0
Improving Unsupervised Stain-To-Stain Translation using Self-Supervision and Meta-Learning—0
In-Context Learning for Few-Shot Molecular Property Prediction—0
Generative Neural Fields by Mixtures of Neural Implicit Functions—0
Generative Meta-Learning for Zero-Shot Relation Triplet Extraction—0
Generative Conversational Networks—0
A Review on Semi-Supervised Relation Extraction—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