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 3451–3500 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
FewSOL: A Dataset for Few-Shot Object Learning in Robotic EnvironmentsCode0
Approximately Equivariant Neural ProcessesCode0
Cross-domain Multi-modal Few-shot Object Detection via Rich TextCode0
Should We Be Pre-training? An Argument for End-task Aware Training as an AlternativeCode0
PICASO: Permutation-Invariant Cascaded Attentional Set OperatorCode0
MetaLR: Meta-tuning of Learning Rates for Transfer Learning in Medical ImagingCode0
Piecewise classifier mappings: Learning fine-grained learners for novel categories with few examplesCode0
Few-Shot Representation Learning for Out-Of-Vocabulary WordsCode0
MetaMedSeg: Volumetric Meta-learning for Few-Shot Organ SegmentationCode0
Meta-Meta Classification for One-Shot LearningCode0
PIED: Physics-Informed Experimental Design for Inverse ProblemsCode0
Feature Extractor Stacking for Cross-domain Few-shot LearningCode0
PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual InformationCode0
Cross-Domain Few-Shot Graph ClassificationCode0
Test-Time Domain Adaptation by Learning Domain-Aware Batch NormalizationCode0
PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical recordsCode0
Few-shot Quality-Diversity OptimizationCode0
PointFix: Learning to Fix Domain Bias for Robust Online Stereo AdaptationCode0
Window Stacking Meta-Models for Clinical EEG ClassificationCode0
Meta-NeighborhoodsCode0
Text normalization using memory augmented neural networksCode0
Meta NetworksCode0
TGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot LearningCode0
Cross-Domain Continual Learning via CLAMPCode0
Few-shot Node Classification with Extremely Weak SupervisionCode0
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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