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

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Showing 651–660 of 3569 papers

TitleStatusHype
Automatic Combination of Sample Selection Strategies for Few-Shot Learning—0
Predicting Configuration Performance in Multiple Environments with Sequential Meta-learningCode0
Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation LearningCode1
Rethinking the Starting Point: Collaborative Pre-Training for Federated Downstream Tasks—0
Human-like Category Learning by Injecting Ecological Priors from Large Language Models into Neural Networks—0
A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt LearningCode1
CPT: Competence-progressive Training Strategy for Few-shot Node Classification—0
Meta-Learning for Neural Network-based Temporal Point Processes—0
Sample Weight Estimation Using Meta-Updates for Online Continual LearningCode0
An Information-Theoretic Analysis of In-Context 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