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 2576–2600 of 3569 papers

TitleStatusHype
Context-Conditioned Spatio-Temporal Predictive Learning for Reliable V2V Channel Prediction—0
Connecting Context-specific Adaptation in Humans to Meta-learning—0
Contextual HyperNetworks for Novel Feature Adaptation—0
Contextual Stochastic Bilevel Optimization—0
Continual Few-Shot Learning with Adversarial Class Storage—0
Continual learning under domain transfer with sparse synaptic bursting—0
Continual Quality Estimation with Online Bayesian Meta-Learning—0
Continuous Learning in a Hierarchical Multiscale Neural Network—0
Continuous-Time Meta-Learning with Forward Mode Differentiation—0
Contrastive Conditional Neural Processes—0
Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification—0
Contrastive Learning is Just Meta-Learning—0
Contrastive Prototype Learning with Augmented Embeddings for Few-Shot Learning—0
Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning—0
Convergence-aware Clustered Federated Graph Learning Framework for Collaborative Inter-company Labor Market Forecasting—0
Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes—0
Convergence of Gradient-based MAML in LQR—0
Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters—0
Convergence Properties of Stochastic Hypergradients—0
MetaCVR: Conversion Rate Prediction via Meta Learning in Small-Scale Recommendation Scenarios—0
Convolutional Neural Networks Can (Meta-)Learn the Same-Different Relation—0
Convolutional Neural Processes for Inpainting Satellite Images—0
Coordinated Control of Deformation and Flight for Morphing Aircraft via Meta-Learning and Coupled State-Dependent Riccati Equations—0
Correction Networks: Meta-Learning for Zero-Shot Learning—0
CosmosDSR -- a methodology for automated detection and tracking of orbital debris using the Unscented Kalman Filter—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