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 1741–1750 of 3569 papers

TitleStatusHype
Learning Unsupervised Learning Rules—0
Learning via Surrogate PAC-Bayes—0
Dynamic Spectral Backpropagation for Efficient Neural Network Training—0
Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms—0
Flow to Learn: Flow Matching on Neural Network Parameters—0
Dynamic Task Weighting Methods for Multi-task Networks in Autonomous Driving Systems—0
Constrained Few-Shot Learning: Human-Like Low Sample Complexity Learning and Non-Episodic Text Classification—0
A Preliminary Study on Using Meta-learning Technique for Information Retrieval—0
Market-Aware Models for Efficient Cross-Market Recommendation—0
Outlier detection using flexible categorisation and interrogative agendas—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