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

Showing 34513475 of 3569 papers

TitleStatusHype
DAMSL: Domain Agnostic Meta Score-based LearningCode0
Meta learning with language models: Challenges and opportunities in the classification of imbalanced textCode0
Fine-Grained Trajectory-based Travel Time Estimation for Multi-city Scenarios Based on Deep Meta-LearningCode0
Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task DistributionsCode0
Adaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label LearningCode0
SHOT: Suppressing the Hessian along the Optimization Trajectory for Gradient-Based Meta-LearningCode0
Should Cross-Lingual AMR Parsing go Meta? An Empirical Assessment of Meta-Learning and Joint Learning AMR ParsingCode0
Personalized Federated Learning with Contextual Modulation and Meta-LearningCode0
Curriculum Meta-Learning for Few-shot ClassificationCode0
Cross-Modal Generalization: Learning in Low Resource Modalities via Meta-AlignmentCode0
Meta-Learning without MemorizationCode0
Meta Learning with Relational Information for Short SequencesCode0
Finding the Homology of Decision Boundaries with Active LearningCode0
Meta-Learning with Shared Amortized Variational InferenceCode0
FIGR: Few-shot Image Generation with ReptileCode0
Personalized Privacy-Preserving Framework for Cross-Silo Federated LearningCode0
Personalizing Dialogue Agents via Meta-LearningCode0
Few 'Zero Level Set'-Shot Learning of Shape Signed Distance Functions in Feature SpaceCode0
Meta-Learning with Variational BayesCode0
Meta-Learning with Variational Semantic Memory for Word Sense DisambiguationCode0
Meta-Learning with Versatile Loss Geometries for Fast Adaptation Using Mirror DescentCode0
Meta-Learning with Warped Gradient DescentCode0
Meta-learnt priors slow down catastrophic forgetting in neural networksCode0
Cross-domain Transfer of Valence Preferences via a Meta-optimization ApproachCode0
Perturbing the Gradient for Alleviating Meta OverfittingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified