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 2076–2100 of 3569 papers

TitleStatusHype
MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning—0
Carle's Game: An Open-Ended Challenge in Exploratory Machine CreativityCode0
Sequential Recommendation for Cold-start Users with Meta Transitional LearningCode1
Meta-learning PINN loss functions—0
Meta-aprendizado para otimizacao de parametros de redes neurais—0
Adaptation of Quadruped Robot Locomotion with Meta-Learning—0
Task Fingerprinting for Meta Learning in Biomedical Image Analysis—0
Learn to Learn Metric Space for Few-Shot Segmentation of 3D Shapes—0
Bias-Tolerant Fair Classification—0
Finding Significant Features for Few-Shot Learning using Dimensionality Reduction—0
Learning an Explicit Hyperparameter Prediction Function Conditioned on TasksCode0
Meta-learning Amidst Heterogeneity and AmbiguityCode0
Bayesian decision-making under misspecified priors with applications to meta-learning—0
Memory Efficient Meta-Learning with Large ImagesCode1
Rapid Neural Architecture Search by Learning to Generate Graphs from DatasetsCode1
Meta-Learning for Relative Density-Ratio Estimation—0
Few-Shot Learning with a Strong TeacherCode1
Resilient UAV Swarm Communications with Graph Convolutional Neural NetworkCode0
How to Train Your MAML to Excel in Few-Shot ClassificationCode1
Relational VAE: A Continuous Latent Variable Model for Graph Structured DataCode1
Representation based meta-learning for few-shot spoken intent recognitionCode0
MAML is a Noisy Contrastive Learner in ClassificationCode1
A Representation Learning Perspective on the Importance of Train-Validation Splitting in Meta-LearningCode0
Meta-learning for Matrix Factorization without Shared Rows or Columns—0
Explaining the Performance of Multi-label Classification Methods with Data Set Properties—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