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 1501–1525 of 3569 papers

TitleStatusHype
Enabling Reproducibility and Meta-learning Through a Lifelong Database of Experiments (LDE)—0
Bias-Tolerant Fair Classification—0
A meta learning scheme for fast accent domain expansion in Mandarin speech recognition—0
Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift—0
A Comprehensive Survey of Dataset Distillation—0
Enabling Deep Learning-based Physical-layer Secret Key Generation for FDD-OFDM Systems in Multi-Environments—0
Enabling Continual Learning in Neural Networks with Meta Learning—0
Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning—0
EMPL: A novel Efficient Meta Prompt Learning Framework for Few-shot Unsupervised Domain Adaptation—0
BiAdam: Fast Adaptive Bilevel Optimization Methods—0
Emotional RobBERT and Insensitive BERTje: Combining Transformers and Affect Lexica for Dutch Emotion Detection—0
EMO: Episodic Memory Optimization for Few-Shot Meta-Learning—0
Beyond Traditional Single Object Tracking: A Survey—0
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning—0
A Meta-Learning Perspective on Transformers for Causal Language Modeling—0
Adaptive Resource Allocation for Virtualized Base Stations in O-RAN with Online Learning—0
Embracing assay heterogeneity with neural processes for markedly improved bioactivity predictions—0
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning—0
Eliminating Meta Optimization Through Self-Referential Meta Learning—0
Beyond Reptile: Meta-Learned Dot-Product Maximization between Gradients for Improved Single-Task Regularization—0
ELF-UA: Efficient Label-Free User Adaptation in Gaze Estimation—0
Electrocardiogram-Language Model for Few-Shot Question Answering with Meta Learning—0
Beyond Induction Heads: In-Context Meta Learning Induces Multi-Phase Circuit Emergence—0
Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning—0
Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approach—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