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 28912900 of 3569 papers

TitleStatusHype
Meta Learning for Causal Direction0
Meta-Semi: A Meta-learning Approach for Semi-supervised Learning0
MetaConcept: Learn to Abstract via Concept Graph for Weakly-Supervised Few-Shot Learning0
On the Outsized Importance of Learning Rates in Local Update MethodsCode0
Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment0
Hypernymy Detection for Low-Resource Languages via Meta Learning0
End-to-End Offline Speech Translation System for IWSLT 2020 using Modality Agnostic Meta-Learning0
End-to-End Simultaneous Translation System for IWSLT2020 Using Modality Agnostic Meta-Learning0
Multi-source Meta Transfer for Low Resource Multiple-Choice Question Answering0
Meta-Reinforced Multi-Domain State Generator for Dialogue Systems0
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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