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 851–875 of 3569 papers

TitleStatusHype
Bayesian-Boosted MetaLoc: Efficient Training and Guaranteed Generalization for Indoor Localization—0
Unleash Model Potential: Bootstrapped Meta Self-supervised Learning—0
MetaWeather: Few-Shot Weather-Degraded Image RestorationCode1
FAM: fast adaptive federated meta-learning—0
The intersection of video capsule endoscopy and artificial intelligence: addressing unique challenges using machine learning—0
MetaGCD: Learning to Continually Learn in Generalized Category DiscoveryCode1
An Entropy-Awareness Meta-Learning Method for SAR Open-Set ATR—0
Meta-learning enhanced next POI recommendation by leveraging check-ins from auxiliary citiesCode0
HyperLoRA for PDEs—0
Meta-ZSDETR: Zero-shot DETR with Meta-learning—0
Generalizable Decision Boundaries: Dualistic Meta-Learning for Open Set Domain GeneralizationCode1
Embracing assay heterogeneity with neural processes for markedly improved bioactivity predictions—0
META-SELD: Meta-Learning for Fast Adaptation to the new environment in Sound Event Localization and Detection—0
Is Meta-Learning the Right Approach for the Cold-Start Problem in Recommender Systems?—0
An Adaptive Approach for Probabilistic Wind Power Forecasting Based on Meta-Learning—0
Dual Meta-Learning with Longitudinally Generalized Regularization for One-Shot Brain Tissue Segmentation Across the Human LifespanCode0
Privacy-preserving Few-shot Traffic Detection against Advanced Persistent Threats via Federated Meta LearningCode1
INR-Arch: A Dataflow Architecture and Compiler for Arbitrary-Order Gradient Computations in Implicit Neural Representation ProcessingCode0
Bayesian Meta-Learning on Control Barrier Functions with Data from On-Board Sensors—0
Cross-heterogeneity Graph Few-shot Learning—0
Sample-Efficient Linear Representation Learning from Non-IID Non-Isotropic Data—0
Extension of Transformational Machine Learning: Classification Problems—0
A Meta-learning based Stacked Regression Approach for Customer Lifetime Value Prediction—0
DaMSTF: Domain Adversarial Learning Enhanced Meta Self-Training for Domain Adaptation—0
Meta-learning in healthcare: A survey—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