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 3301–3350 of 3569 papers

TitleStatusHype
Learning What and Where to TransferCode0
Meta-Learning for Low-resource Natural Language Generation in Task-oriented Dialogue Systems—0
Hierarchically Structured Meta-learningCode0
Few-Shot Viewpoint Estimation—0
Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category GraphCode0
Meta-learning of Sequential Strategies—0
Data-Efficient Mutual Information Neural Estimator—0
MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health RecordsCode0
Interpretable Automated Machine Learning in Maana(TM) Knowledge Platform—0
Few-Shot Adaptive Gaze EstimationCode0
Meta-learners' learning dynamics are unlike learners'—0
Modulating transfer between tasks in gradient-based meta-learning—0
MahiNet: A Neural Network for Many-Class Few-Shot Learning with Class Hierarchy—0
Learning a Meta-Solver for Syntax-Guided Program Synthesis—0
Meta-Learning to Guide Segmentation—0
Learning from Noisy Demonstration Sets via Meta-Learned Suitability Assessor—0
Meta-Learning with Domain Adaptation for Few-Shot Learning under Domain Shift—0
Projective Subspace Networks For Few-Shot Learning—0
Attentive Task-Agnostic Meta-Learning for Few-Shot Text Classification—0
Learning to Reinforcement Learn by Imitation—0
Guided Evolutionary Strategies: Escaping the curse of dimensionality in random search—0
Learning Unsupervised Learning Rules—0
Transferring SLU Models in Novel Domains—0
Amortized Bayesian Meta-Learning—0
Sample-efficient policy learning in multi-agent Reinforcement Learning via meta-learning—0
Learning to Learn with Conditional Class Dependencies—0
Learning Meta Model for Zero- and Few-shot Face Anti-spoofing—0
META-Learning State-based Eligibility Traces for More Sample-Efficient Policy EvaluationCode0
Hierarchical Meta Learning—0
Meta-Weighted Gaussian Process Experts for Personalized Forecasting of AD Cognitive Changes—0
Online Adaptation through Meta-Learning for Stereo Depth Estimation—0
An Ensemble of Epoch-wise Empirical Bayes for Few-shot LearningCode0
ACE: Adapting to Changing Environments for Semantic Segmentation—0
TAFE-Net: Task-Aware Feature Embeddings for Low Shot LearningCode0
MxML: Mixture of Meta-Learners for Few-Shot Classification—0
Few-Shot Learning with Localization in Realistic SettingsCode0
L2AE-D: Learning to Aggregate Embeddings for Few-shot Learning with Meta-level Dropout—0
Compare More Nuanced:Pairwise Alignment Bilinear Network For Few-shot Fine-grained LearningCode0
Actively Seeking and Learning from Live Data—0
Meta-Learning Acquisition Functions for Transfer Learning in Bayesian OptimizationCode0
A Hybrid Approach with Optimization and Metric-based Meta-Learner for Few-Shot Learning—0
Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage DatasetCode0
Guided Meta-Policy Search—0
Adaptive Bayesian Linear Regression for Automated Machine Learning—0
Meta-Learning surrogate models for sequential decision making—0
Diversity with Cooperation: Ensemble Methods for Few-Shot ClassificationCode0
A Pseudo-Label Method for Coarse-to-Fine Multi-Label Learning with Limited Supervision—0
Few-Shot Regression via Learned Basis Functions—0
Enhancing Generalization of First-Order Meta-Learning—0
Learning Feature Relevance Through Step Size Adaptation in Temporal-Difference Learning—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