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 1601–1625 of 3569 papers

TitleStatusHype
Augmenting Medical Imaging: A Comprehensive Catalogue of 65 Techniques for Enhanced Data Analysis—0
First-order ANIL provably learns representations despite overparametrization—0
Optimization-Based Deep learning methods for Magnetic Resonance Imaging Reconstruction and SynthesisCode0
Learning to Retain while Acquiring: Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation—0
Meta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start RecommendationCode0
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
MONGOOSE: Path-wise Smooth Bayesian Optimisation via Meta-learning—0
Personalized Privacy-Preserving Framework for Cross-Silo Federated LearningCode0
Mask-guided BERT for Few Shot Text Classification—0
CMVAE: Causal Meta VAE for Unsupervised Meta-LearningCode0
Deep Neural Networks based Meta-Learning for Network Intrusion Detection—0
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
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
Meta-Learning Based Knowledge Extrapolation for Temporal Knowledge Graph—0
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation—0
APAM: Adaptive Pre-training and Adaptive Meta Learning in Language Model for Noisy Labels and Long-tailed Learning—0
Cross-Frequency Time Series Meta-Forecasting—0
Efficient Gradient Approximation Method for Constrained Bilevel Optimization—0
Double Equivariance for Inductive Link Prediction for Both New Nodes and New Relation TypesCode0
Algorithm Design for Online Meta-Learning with Task Boundary Detection—0
MetaTKG: Learning Evolutionary Meta-Knowledge for Temporal Knowledge Graph Reasoning—0
Meta Learning in Decentralized Neural Networks: Towards More General AI—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