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 1651–1675 of 3569 papers

TitleStatusHype
FRANS: Automatic Feature Extraction for Time Series Forecasting—0
Arbitrary Order Meta-Learning with Simple Population-Based Evolution—0
Learning to generate imaginary tasks for improving generalization in meta-learning—0
Domain Generalization through Meta-Learning: A Survey—0
Learning to Identify Physical Laws of Hamiltonian Systems via Meta-Learning—0
Learning to Infer Counterfactuals: Meta-Learning for Estimating Multiple Imbalanced Treatment Effects—0
Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?—0
Learning to Learn a Cold-start Sequential Recommender—0
Foundations of Cyber Resilience: The Confluence of Game, Control, and Learning Theories—0
Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification—0
Lesion2Vec: Deep Metric Learning for Few-Shot Multiple Lesions Recognition in Wireless Capsule Endoscopy Video—0
A Pseudo-Label Method for Coarse-to-Fine Multi-Label Learning with Limited Supervision—0
FORML: Learning to Reweight Data for Fairness—0
Learn to Unlearn: Meta-Learning-Based Knowledge Graph Embedding Unlearning—0
FORML: A Riemannian Hessian-free Method for Meta-learning on Stiefel Manifolds—0
Learn to Sense: a Meta-learning Based Sensing and Fusion Framework for Wireless Sensor Networks—0
Don’t Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights—0
Learning to Learn Dense Gaussian Processes for Few-Shot Learning—0
Leaving No One Behind: A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling—0
Learning to Learn End-to-End Goal-Oriented Dialog From Related Dialog Tasks—0
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification—0
Leveraging Auxiliary Task Relevance for Enhanced Bearing Fault Diagnosis through Curriculum Meta-learning—0
Double Meta-Learning for Data Efficient Policy Optimization in Non-Stationary Environments—0
Learning to Learn Group Alignment: A Self-Tuning Credo Framework with Multiagent Teams—0
Constructing a meta-learner for unsupervised anomaly detection—0
Show:102550
← PrevPage 67 of 143Next →
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