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

Showing 111120 of 3569 papers

TitleStatusHype
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot LearningCode1
An Accurate and Fully-Automated Ensemble Model for Weekly Time Series ForecastingCode1
Delving Deep Into Many-to-Many Attention for Few-Shot Video Object SegmentationCode1
ArtFID: Quantitative Evaluation of Neural Style TransferCode1
Adaptive Risk Minimization: Learning to Adapt to Domain ShiftCode1
A Brain Graph Foundation Model: Pre-Training and Prompt-Tuning for Any Atlas and DisorderCode1
A Simple Approach to Case-Based Reasoning in Knowledge BasesCode1
Diffusion-Based Neural Network Weights GenerationCode1
BlackGoose Rimer: Harnessing RWKV-7 as a Simple yet Superior Replacement for Transformers in Large-Scale Time Series ModelingCode1
A Meta-Learning Approach for Graph Representation Learning in Multi-Task SettingsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified