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 1781–1790 of 3569 papers

TitleStatusHype
Minimax and Neyman–Pearson Meta-Learning for Outlier Languages—0
Minimizing Memorization in Meta-learning: A Causal Perspective—0
Mining Recurrent Concepts in Data Streams using the Discrete Fourier Transform—0
Missing Modality meets Meta Sampling (M3S): An Efficient Universal Approach for Multimodal Sentiment Analysis with Missing Modality—0
Mitigating Spurious Correlations with Causal Logit Perturbation—0
RegMix: Data Mixing Augmentation for Regression—0
Mixture of Experts in Large Language Models—0
MLDGG: Meta-Learning for Domain Generalization on Graphs—0
ML-misfit: Learning a robust misfit function for full-waveform inversion using machine learning—0
MMformer with Adaptive Transferable Attention: Advancing Multivariate Time Series Forecasting for Environmental Applications—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