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 2476–2500 of 3569 papers

TitleStatusHype
LiMAML: Personalization of Deep Recommender Models via Meta Learning—0
Localization of Coordinated Cyber-Physical Attacks in Power Grids Using Moving Target Defense and Deep Learning—0
Localized Generations with Deep Neural Networks for Multi-Scale Structured Datasets—0
Localized Meta-Learning: A PAC-Bayes Analysis for Meta-Learning Beyond Global Prior—0
Localized Meta-Learning: A PAC-Bayes Analysis for Meta-Leanring Beyond Global Prior—0
Local transfer learning from one data space to another—0
LogAnMeta: Log Anomaly Detection Using Meta Learning—0
LogitMat : Zeroshot Learning Algorithm for Recommender Systems without Transfer Learning or Pretrained Models—0
Long Short-Term Temporal Meta-learning in Online Recommendation—0
Looking back to lower-level information in few-shot learning—0
Loss Function Learning for Domain Generalization by Implicit Gradient—0
Loss meta-learning for forecasting—0
Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsing—0
Low-Resource Multilingual and Zero-Shot Multispeaker TTS—0
Low-Resource Named Entity Recognition: Can One-vs-All AUC Maximization Help?—0
Low Resource Style Transfer via Domain Adaptive Meta Learning—0
Low Resource Style Transfer via Domain Adaptive Meta Learning—0
M2I2: Learning Efficient Multi-Agent Communication via Masked State Modeling and Intention Inference—0
MAC: A Meta-Learning Approach for Feature Learning and Recombination—0
Machine Learning: a Lecture Note—0
Machine Learning Approaches For Motor Learning: A Short Review—0
Machine Theory of Mind—0
MADOD: Generalizing OOD Detection to Unseen Domains via G-Invariance Meta-Learning—0
MahiNet: A Neural Network for Many-Class Few-Shot Learning with Class Hierarchy—0
Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning—0
Show:102550
← PrevPage 100 of 143Next →
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