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

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Showing 691–700 of 3569 papers

TitleStatusHype
Hybrid-Task Meta-Learning: A Graph Neural Network Approach for Scalable and Transferable Bandwidth Allocation—0
Meta-Learning-Based Adaptive Stability Certificates for Dynamical SystemsCode0
Personalized Federated Learning with Contextual Modulation and Meta-LearningCode0
Discovering modular solutions that generalize compositionallyCode1
Meta Transfer of Self-Supervised Knowledge: Foundation Model in Action for Post-Traumatic Epilepsy Prediction—0
Meta-Learning with Versatile Loss Geometries for Fast Adaptation Using Mirror DescentCode0
AutoXPCR: Automated Multi-Objective Model Selection for Time Series ForecastingCode0
Scaling Opponent Shaping to High Dimensional Games—0
POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning—0
Outlier detection using flexible categorisation and interrogative agendas—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