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 351–375 of 3569 papers

TitleStatusHype
Meta-Transfer Learning Empowered Temporal Graph Networks for Cross-City Real Estate Appraisal—0
Meta-Learning-Driven Adaptive Codebook Design for Near-Field Communications—0
Metalic: Meta-Learning In-Context with Protein Language ModelsCode1
Meta-Learning from Learning Curves for Budget-Limited Algorithm Selection—0
Meta-Learning Integration in Hierarchical Reinforcement Learning for Advanced Task ComplexityCode0
System 2 Reasoning via Generality and Adaptation—0
ConML: A Universal Meta-Learning Framework with Task-Level Contrastive Learning—0
Meta-Dynamical State Space Models for Integrative Neural Data Analysis—0
A Cross-Lingual Meta-Learning Method Based on Domain Adaptation for Speech Emotion Recognition—0
Gödel Agent: A Self-Referential Agent Framework for Recursive Self-ImprovementCode2
Improving Arabic Multi-Label Emotion Classification using Stacked Embeddings and Hybrid Loss FunctionCode0
Should Cross-Lingual AMR Parsing go Meta? An Empirical Assessment of Meta-Learning and Joint Learning AMR ParsingCode0
Zebra: In-Context and Generative Pretraining for Solving Parametric PDEs—0
MetaOOD: Automatic Selection of OOD Detection Models—0
Unsupervised Meta-Learning via Dynamic Head and Heterogeneous Task Construction for Few-Shot ClassificationCode0
Learning to learn ecosystems from limited data -- a meta-learning approachCode0
Reducing Variance in Meta-Learning via Laplace Approximation for Regression Tasks—0
Meta-TTT: A Meta-learning Minimax Framework For Test-Time Training—0
Reevaluating Meta-Learning Optimization Algorithms Through Contextual Self-ModulationCode0
Recovering Time-Varying Networks From Single-Cell Data—0
Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RAN—0
PersonalLLM: Tailoring LLMs to Individual PreferencesCode1
Meta Learning to Rank for Sparsely Supervised Queries—0
Convergence-aware Clustered Federated Graph Learning Framework for Collaborative Inter-company Labor Market Forecasting—0
Meta-RTL: Reinforcement-Based Meta-Transfer Learning for Low-Resource Commonsense Reasoning—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