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 291–300 of 3569 papers

TitleStatusHype
QUOTA: Quantifying Objects with Text-to-Image Models for Any Domain—0
MetaGraphLoc: A Graph-based Meta-learning Scheme for Indoor Localization via Sensor Fusion—0
On the ERM Principle in Meta-Learning—0
MetaCropFollow: Few-Shot Adaptation with Meta-Learning for Under-Canopy Navigation—0
Material synthesis through simulations guided by machine learning: a position paper—0
AdaptAgent: Adapting Multimodal Web Agents with Few-Shot Learning from Human Demonstrations—0
MLDGG: Meta-Learning for Domain Generalization on Graphs—0
Online Item Cold-Start Recommendation with Popularity-Aware Meta-LearningCode0
Effective Predictive Modeling for Emergency Department Visits and Evaluating Exogenous Variables Impact: Using Explainable Meta-learning Gradient Boosting—0
AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with TransformersCode2
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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