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

Showing 12511275 of 3569 papers

TitleStatusHype
Advancing Open-Set Domain Generalization Using Evidential Bi-Level Hardest Domain SchedulerCode0
Generalizing Reward Modeling for Out-of-Distribution Preference LearningCode0
Clustering Indices based Automatic Classification Model SelectionCode0
General-Purpose In-Context Learning by Meta-Learning TransformersCode0
Inverse Learning with Extremely Sparse Feedback for RecommendationCode0
Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain SetupsCode0
Latent Bottlenecked Attentive Neural ProcessesCode0
Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal PredictionCode0
Learning to Generate Noise for Multi-Attack RobustnessCode0
Few-Shot Classification of Skin Lesions from Dermoscopic Images by Meta-Learning Representative EmbeddingsCode0
Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image RecognitionCode0
Clustered Task-Aware Meta-Learning by Learning from Learning PathsCode0
INR-Arch: A Dataflow Architecture and Compiler for Arbitrary-Order Gradient Computations in Implicit Neural Representation ProcessingCode0
Few-shot classification in Named Entity Recognition TaskCode0
Meta-Graph: Few Shot Link Prediction via Meta LearningCode0
Fast Few-Shot Classification by Few-Iteration Meta-LearningCode0
Closed-form Sample Probing for Learning Generative Models in Zero-shot LearningCode0
Few-Shot Character Understanding in Movies as an Assessment to Meta-Learning of Theory-of-MindCode0
Incremental Few-Shot Learning with Attention Attractor NetworksCode0
Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry DetectionCode0
Few-Shot Calibration of Set Predictors via Meta-Learned Cross-Validation-Based Conformal PredictionCode0
Few-shot calibration of low-cost air pollution (PM2.5) sensors using meta-learningCode0
CLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy LabelsCode0
Improving Meta-Learning Generalization with Activation-Based Early-StoppingCode0
Active exploration in parameterized reinforcement learningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified