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 371–380 of 3569 papers

TitleStatusHype
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
Advancing Open-Set Domain Generalization Using Evidential Bi-Level Hardest Domain SchedulerCode0
Reducing and Exploiting Data Augmentation Noise through Meta Reweighting Contrastive Learning for Text Classification—0
EvoFA: Evolvable Fast Adaptation for EEG Emotion Recognition—0
Hierarchical end-to-end autonomous navigation through few-shot waypoint detection—0
From Text to Treatment Effects: A Meta-Learning Approach to Handling Text-Based Confounding—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