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 2371–2380 of 3569 papers

TitleStatusHype
An Information-Theoretic Analysis of In-Context Learning—0
An Information-Theoretic Analysis of the Impact of Task Similarity on Meta-Learning—0
Self-Programming Artificial Intelligence Using Code-Generating Language Models—0
An Introduction to Advanced Machine Learning : Meta Learning Algorithms, Applications and Promises—0
Anomaly Detection with Ensemble of Encoder and Decoder—0
An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset—0
A Novel Meta Learning Framework for Feature Selection using Data Synthesis and Fuzzy Similarity—0
A novel meta-learning initialization method for physics-informed neural networks—0
A Novel Semi-supervised Meta Learning Method for Subject-transfer Brain-computer Interface—0
Any-Way 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