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 1211–1220 of 3569 papers

TitleStatusHype
MetaSplit: Meta-Split Network for Limited-Stock Product Recommendation—0
All in One: Multi-Task Prompting for Graph Neural Networks (Extended Abstract)—0
Provable Multi-Party Reinforcement Learning with Diverse Human Feedback—0
Rethinking of Encoder-based Warm-start Methods in Hyperparameter OptimizationCode0
Boosting Meta-Training with Base Class Information for Few-Shot Learning—0
Learning to Defer to a Population: A Meta-Learning ApproachCode0
Decomposed Meta-Learning for Few-Shot Sequence LabelingCode0
Transformers for Supervised Online Continual Learning—0
CCML: Curriculum and Contrastive Learning Enhanced Meta-Learner for Personalized Spatial Trajectory Prediction—0
BP-DeepONet: A new method for cuffless blood pressure estimation using the physcis-informed DeepONet—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