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 211–220 of 3569 papers

TitleStatusHype
Meta-Learning Biologically Plausible Plasticity Rules with Random Feedback PathwaysCode1
m^4Adapter: Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-AdapterCode1
Incorporating Relevance Feedback for Information-Seeking Retrieval using Few-Shot Document Re-RankingCode1
Continued Pretraining for Better Zero- and Few-Shot PromptabilityCode1
Meta-Learning via Classifier(-free) Diffusion GuidanceCode1
Few-shot Relational Reasoning via Connection Subgraph PretrainingCode1
Stock Trading Volume Prediction with Dual-Process Meta-LearningCode1
Meta-Learning with Self-Improving Momentum TargetCode1
CD-FSOD: A Benchmark for Cross-domain Few-shot Object DetectionCode1
DIMES: A Differentiable Meta Solver for Combinatorial Optimization ProblemsCode1
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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