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 3241–3250 of 3569 papers

TitleStatusHype
Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph CompletionCode0
Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated LearningCode0
On-device Online Learning and Semantic Management of TinyML SystemsCode0
Deep Mixture of Experts via Shallow EmbeddingCode0
Meta-Learning for Effective Multi-task and Multilingual ModellingCode0
Meta-Learning for Efficient Fine-Tuning of Large Language ModelsCode0
Meta Learning for Efficient Fine-Tuning of Large Language ModelsCode0
TAFE-Net: Task-Aware Feature Embeddings for Low Shot LearningCode0
Meta-learning for fast classifier adaptation to new users of Signature Verification systemsCode0
Meta-Learning for Fast Cross-Lingual Adaptation in Dependency ParsingCode0
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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