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 2391–2400 of 3569 papers

TitleStatusHype
Principal component analysis for Gaussian process posteriors—0
MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning—0
Stacking hybrid GARCH models for forecasting Bitcoin volatility—0
Carle's Game: An Open-Ended Challenge in Exploratory Machine CreativityCode0
Meta-learning PINN loss functions—0
Meta-aprendizado para otimizacao de parametros de redes neurais—0
Adaptation of Quadruped Robot Locomotion with Meta-Learning—0
Task Fingerprinting for Meta Learning in Biomedical Image Analysis—0
Learn to Learn Metric Space for Few-Shot Segmentation of 3D Shapes—0
Bias-Tolerant Fair Classification—0
Show:102550
← PrevPage 240 of 357Next →
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