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 2526–2550 of 3569 papers

TitleStatusHype
Chain of Thought with Explicit Evidence Reasoning for Few-shot Relation Extraction—0
Challenge Closed-book Science Exam: A Meta-learning Based Question Answering System—0
Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images—0
Characterizing Policy Divergence for Personalized Meta-Reinforcement Learning—0
CHOMET: Conditional Handovers via Meta-Learning—0
CICADA: Cross-Domain Interpretable Coding for Anomaly Detection and Adaptation in Multivariate Time Series—0
Semantic Regularization: Improve Few-shot Image Classification by Reducing Meta Shift—0
Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach—0
CLIP-aware Domain-Adaptive Super-Resolution—0
Clustering-based Meta Bayesian Optimization with Theoretical Guarantee—0
ClusterSeq: Enhancing Sequential Recommender Systems with Clustering based Meta-Learning—0
Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation—0
CML: A Contrastive Meta Learning Method to Estimate Human Label Confidence Scores and Reduce Data Collection Cost—0
Towards Cross-Granularity Few-Shot Learning: Coarse-to-Fine Pseudo-Labeling with Visual-Semantic Meta-Embedding—0
CognitiveNet: Enriching Foundation Models with Emotions and Awareness—0
Cold-start Sequential Recommendation via Meta Learner—0
Cold & Warm Net: Addressing Cold-Start Users in Recommender Systems—0
Collaborative Imputation of Urban Time Series through Cross-city Meta-learning—0
CoLLEGe: Concept Embedding Generation for Large Language Models—0
Combat Data Shift in Few-shot Learning with Knowledge Graph—0
Combining Adversaries with Anti-adversaries in Training—0
Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review—0
Combining Domain-Specific Meta-Learners in the Parameter Space for Cross-Domain Few-Shot Classification—0
Combining Forecasts using Meta-Learning: A Comparative Study for Complex Seasonality—0
Combining One-Class Classifiers via 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