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 3551–3560 of 3569 papers

TitleStatusHype
Adaptive Ensemble Learning with Confidence Bounds—0
A DEEP analysis of the META-DES framework for dynamic selection of ensemble of classifiers—0
Distributed Representations of Words and Documents for Discriminating Similar Languages—0
Theoretical and Empirical Analysis of a Parallel Boosting Algorithm—0
Semi-Stacking for Semi-supervised Sentiment Classification—0
Stacked Generalization for Medical Concept Extraction from Clinical Notes—0
Meta learning of bounds on the Bayes classifier error—0
Image Retrieval And Classification Using Local Feature Vectors—0
SimCompass: Using Deep Learning Word Embeddings to Assess Cross-level Similarity—0
Recommending Learning Algorithms and Their Associated Hyperparameters—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