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 1221–1230 of 3569 papers

TitleStatusHype
Hyperbolic Graph Neural Networks at Scale: A Meta Learning Approach—0
Assessing two novel distance-based loss functions for few-shot image classification—0
A History of Meta-gradient: Gradient Methods for Meta-learning—0
Cross-Lingual Transfer with MAML on Trees—0
Cross-lingual Adaption Model-Agnostic Meta-Learning for Natural Language Understanding—0
A Single-Timescale Method for Stochastic Bilevel Optimization—0
Hyperbolic Dual Feature Augmentation for Open-Environment—0
HyperDynamics: Meta-Learning Object and Agent Dynamics with Hypernetworks—0
Cross-heterogeneity Graph Few-shot Learning—0
Cross-Frequency Time Series Meta-Forecasting—0
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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