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 1201–1225 of 3569 papers

TitleStatusHype
DAML-ST5: Low Resource Style Transfer via Domain Adaptive Meta Learning—0
A Hybrid Model for Few-Shot Text Classification Using Transfer and Meta-Learning—0
3D Meta-Registration: Learning to Learn Registration of 3D Point Clouds—0
HyPer-EP: Meta-Learning Hybrid Personalized Models for Cardiac Electrophysiology—0
DAML: Chinese Named Entity Recognition with a fusion method of data-augmentation and meta-learning—0
Customized Conversational Recommender Systems—0
A Stochastic Approach to Bi-Level Optimization for Hyperparameter Optimization and Meta Learning—0
A Hybrid Meta-Learning and Multi-Armed Bandit Approach for Context-Specific Multi-Objective Recommendation Optimization—0
Curriculum Meta-Learning for Next POI Recommendation—0
A statistical physics framework for optimal learning—0
Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation—0
Curriculum in Gradient-Based Meta-Reinforcement Learning—0
A Hybrid Architecture with Efficient Fine Tuning for Abstractive Patent Document Summarization—0
Hyperbolic Dual Feature Augmentation for Open-Environment—0
HyperLoRA for PDEs—0
HyperSBINN: A Hypernetwork-Enhanced Systems Biology-Informed Neural Network for Efficient Drug Cardiosafety Assessment—0
Crowdsourcing with Meta-Workers: A New Way to Save the Budget—0
Cross-subject Action Unit Detection with Meta Learning and Transformer-based Relation Modeling—0
Adaptive Clipping for Privacy-Preserving Few-Shot Learning: Enhancing Generalization with Limited Data—0
Cross-Platform and Cross-Domain Abusive Language Detection with Supervised Contrastive Learning—0
cross-modal knowledge enhancement mechanism for few-shot learning—0
A Hybrid Approach with Optimization and Metric-based Meta-Learner for Few-Shot Learning—0
Hybrid Control Policy for Artificial Pancreas via Ensemble Deep Reinforcement Learning—0
Assessing two novel distance-based loss functions for few-shot image classification—0
A History of Meta-gradient: Gradient Methods for Meta-learning—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