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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1101–1125 of 3569 papers

TitleStatusHype
Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot LearningCode1
MetaLDC: Meta Learning of Low-Dimensional Computing Classifiers for Fast On-Device Adaption—0
Bayes meets Bernstein at the Meta Level: an Analysis of Fast Rates in Meta-Learning with PAC-Bayes—0
Personalized Privacy-Preserving Framework for Cross-Silo Federated LearningCode0
MONGOOSE: Path-wise Smooth Bayesian Optimisation via Meta-learning—0
Mask-guided BERT for Few Shot Text Classification—0
Nystrom Method for Accurate and Scalable Implicit DifferentiationCode1
Incremental Few-Shot Object Detection via Simple Fine-Tuning ApproachCode1
CMVAE: Causal Meta VAE for Unsupervised Meta-LearningCode0
Deep Neural Networks based Meta-Learning for Network Intrusion Detection—0
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image ClassificationCode1
Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?—0
A Meta-Learning Approach to Population-Based Modelling of Structures—0
Learning from Noisy Labels with Decoupled Meta Label PurifierCode1
Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity RecognitionCode1
Market-Aware Models for Efficient Cross-Market Recommendation—0
Stabilized In-Context Learning with Pre-trained Language Models for Few Shot Dialogue State Tracking—0
Procedural generation of meta-reinforcement learning tasksCode1
Meta-Learning Based Knowledge Extrapolation for Temporal Knowledge Graph—0
On Penalty-based Bilevel Gradient Descent MethodCode1
Hypernetworks build Implicit Neural Representations of SoundsCode1
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation—0
Memory-Based Meta-Learning on Non-Stationary DistributionsCode1
APAM: Adaptive Pre-training and Adaptive Meta Learning in Language Model for Noisy Labels and Long-tailed Learning—0
Meta-Learning Siamese Network for Few-Shot Text ClassificationCode1
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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