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 3101–3150 of 3569 papers

TitleStatusHype
Resilient UAV Swarm Communications with Graph Convolutional Neural NetworkCode0
Torchmeta: A Meta-Learning library for PyTorchCode0
RESUS: Warm-Up Cold Users via Meta-Learning Residual User Preferences in CTR PredictionCode0
Submodular Meta-LearningCode0
Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit FeedbackCode0
Bayesian Meta-Learning for Improving Generalizability of Health Prediction Models With Similar Causal MechanismsCode0
Efficient time stepping for numerical integration using reinforcement learningCode0
Subspace Adaptation Prior for Few-Shot LearningCode0
Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-LearningCode0
MetaFun: Meta-Learning with Iterative Functional UpdatesCode0
Carle's Game: An Open-Ended Challenge in Exploratory Machine CreativityCode0
Improving Meta-Learning Generalization with Activation-Based Early-StoppingCode0
MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly DetectionCode0
Efficient Optimization of Loops and Limits with Randomized Telescoping SumsCode0
B-SMALL: A Bayesian Neural Network approach to Sparse Model-Agnostic Meta-LearningCode0
Improving Meta-Continual Learning Representations with Representation ReplayCode0
Regression Networks for Meta-Learning Few-Shot ClassificationCode0
Rethinking of Encoder-based Warm-start Methods in Hyperparameter OptimizationCode0
A Neural-Symbolic Architecture for Inverse Graphics Improved by Lifelong Meta-LearningCode0
Toward Evaluative Thinking: Meta Policy Optimization with Evolving Reward ModelsCode0
Improving Memory Efficiency for Training KANs via Meta LearningCode0
Meta-GNN: On Few-shot Node Classification in Graph Meta-learningCode0
Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-TrainingCode0
Improving Generalization in Meta-Learning via Meta-Gradient AugmentationCode0
Theoretical Convergence of Multi-Step Model-Agnostic Meta-LearningCode0
Meta-Gradient Reinforcement LearningCode0
Meta-Graph: Few Shot Link Prediction via Meta LearningCode0
A Cost-Sensitive Meta-Learning Strategy for Fair Provider Exposure in RecommendationCode0
MetaGreen: Meta-Learning Inspired Transformer Selection for Green Semantic CommunicationCode0
Bottom-Up Meta-Policy SearchCode0
When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text ClassificationCode0
Improving Federated Learning Personalization via Model Agnostic Meta LearningCode0
Reusable Options through Gradient-based Meta LearningCode0
Improving Both Domain Robustness and Domain Adaptability in Machine TranslationCode0
Unsupervised Meta-learning of Figure-Ground Segmentation via Imitating Visual EffectsCode0
Multi-task Maximum Entropy Inverse Reinforcement LearningCode0
Meta-Information Guided Meta-Learning for Few-Shot Relation ClassificationCode0
Multi-Task Meta Learning: learn how to adapt to unseen tasksCode0
Meta-INR: Efficient Encoding of Volumetric Data via Meta-Learning Implicit Neural RepresentationCode0
Improving Arabic Multi-Label Emotion Classification using Stacked Embeddings and Hybrid Loss FunctionCode0
Simultaneous Perturbation Method for Multi-Task Weight Optimization in One-Shot Meta-LearningCode0
Effective Structured Prompting by Meta-Learning and Representative VerbalizerCode0
Improve Meta-learning for Few-Shot Text Classification with All You Can Acquire from the TasksCode0
Zero-Shot Task TransferCode0
MetaKernel: Learning Variational Random Features with Limited LabelsCode0
Im-Promptu: In-Context Composition from Image PromptsCode0
Multi-view Distillation based on Multi-modal Fusion for Few-shot Action Recognition(CLIP-M^2DF)Code0
Meta Label Correction for Noisy Label LearningCode0
MetAL: Active Semi-Supervised Learning on Graphs via Meta LearningCode0
Bootstrapping Informative Graph Augmentation via A Meta Learning ApproachCode0
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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