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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 1776–1800 of 3569 papers

TitleStatusHype
Meta-ZSDETR: Zero-shot DETR with Meta-learning—0
Metric-based multimodal meta-learning for human movement identification via footstep recognition—0
MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text Classification—0
MICK: A Meta-Learning Framework for Few-shot Relation Classification with Small Training Data—0
Minimally Naturalistic Artificial Intelligence—0
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
MM-FSOD: Meta and metric integrated few-shot object detection—0
MMTL: The Meta Multi-Task Learning for Aspect Category Sentiment Analysis—0
MSD: Saliency-aware Knowledge Distillation for Multimodal Understanding—0
Modality-specific Distillation—0
Model-Agnostic Graph Regularization for Few-Shot Learning—0
Model-Agnostic Meta-Learning for EEG Motor Imagery Decoding in Brain-Computer-Interfacing—0
Model-Agnostic Meta-Learning for Fault Diagnosis of Induction Motors in Data-Scarce Environments with Varying Operating Conditions and Electric Drive Noise—0
Model-Agnostic Meta-Learning for Multilingual Hate Speech Detection—0
Model-Agnostic Meta-Learning for Multimodal Task Distributions—0
Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance—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