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 3461–3470 of 3569 papers

TitleStatusHype
Adversarial Meta-Learning—0
Probabilistic Model-Agnostic Meta-Learning—0
Meta-Learning by the Baldwin Effect—0
Deep Mixture of Experts via Shallow EmbeddingCode0
Meta-Learner with Linear Nulling—0
On the Importance of Attention in Meta-Learning for Few-Shot Text Classification—0
Text normalization using memory augmented neural networksCode0
Learning to Propagate Labels: Transductive Propagation Network for Few-shot LearningCode0
Analysing Symbolic Regression Benchmarks under a Meta-Learning Approach—0
Lifelong Domain Word Embedding via Meta-LearningCode0
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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