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 20912100 of 3569 papers

TitleStatusHype
SalesRLAgent: A Reinforcement Learning Approach for Real-Time Sales Conversion Prediction and Optimization0
Sample Compression Hypernetworks: From Generalization Bounds to Meta-Learning0
Sample Efficient Adaptive Text-to-Speech0
Sample Efficient Linear Meta-Learning by Alternating Minimization0
Sample-efficient policy learning in multi-agent Reinforcement Learning via meta-learning0
Sample Efficient Subspace-based Representations for Nonlinear Meta-Learning0
SA-NET.v2: Real-time vehicle detection from oblique UAV images with use of uncertainty estimation in deep meta-learning0
SAVME: Efficient Safety Validation for Autonomous Systems Using Meta-Learning0
SB-MTL: Score-based Meta Transfer-Learning for Cross-Domain Few-Shot Learning0
Scalable Meta-Learning via Mixed-Mode Differentiation0
Show:102550
← PrevPage 210 of 357Next →

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