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 1051–1060 of 3569 papers

TitleStatusHype
Geometry Aware Meta-Learning Neural Network for Joint Phase and Precoder Optimization in RIS—0
Homomorphisms Between Transfer, Multi-Task, and Meta-Learning Systems—0
Digital Twin-Empowered Network Planning for Multi-Tier Computing—0
AutoLoRA: Automatically Tuning Matrix Ranks in Low-Rank Adaptation Based on Meta Learning—0
Dif-MAML: Decentralized Multi-Agent Meta-Learning—0
Alpha MAML: Adaptive Model-Agnostic Meta-Learning—0
Difficulty-aware Meta-learning for Rare Disease Diagnosis—0
Differentially Private Meta-Learning—0
Differentially Private Bilevel Optimization: Efficient Algorithms with Near-Optimal Rates—0
Adaptive Gradient-Based Meta-Learning Methods—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