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 726–750 of 3569 papers

TitleStatusHype
A Meta-Reinforcement Learning Approach to Process Control—0
Bilevel Continual Learning—0
Bias-Tolerant Fair Classification—0
A meta learning scheme for fast accent domain expansion in Mandarin speech recognition—0
End-to-End Simultaneous Translation System for IWSLT2020 Using Modality Agnostic Meta-Learning—0
Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning—0
BiAdam: Fast Adaptive Bilevel Optimization Methods—0
Adaptive Resource Allocation for Virtualized Base Stations in O-RAN with Online Learning—0
Beyond Traditional Single Object Tracking: A Survey—0
A Meta-Learning Perspective on Transformers for Causal Language Modeling—0
Enabling Deep Learning-based Physical-layer Secret Key Generation for FDD-OFDM Systems in Multi-Environments—0
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning—0
Beyond Reptile: Meta-Learned Dot-Product Maximization between Gradients for Improved Single-Task Regularization—0
A Meta-Learning Perspective on Cold-Start Recommendations for Items—0
Beyond Induction Heads: In-Context Meta Learning Induces Multi-Phase Circuit Emergence—0
Beyond Exponentially Discounted Sum: Automatic Learning of Return Function—0
Adaptive Reinforcement Learning through Evolving Self-Modifying Neural Networks—0
A Survey on Curriculum Learning—0
Enabling Reproducibility and Meta-learning Through a Lifelong Database of Experiments (LDE)—0
Beyond Bayes-optimality: meta-learning what you know you don't know—0
Betty: An Automatic Differentiation Library for Multilevel Optimization—0
A Meta-learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning—0
BERT Learns to Teach: Knowledge Distillation with Meta Learning—0
Bending the Curve: Improving the ROC Curve Through Error Redistribution—0
A Blockchain-based Reliable Federated Meta-learning for Metaverse: A Dual Game Framework—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