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 326350 of 3569 papers

TitleStatusHype
Grounded Language Learning Fast and SlowCode1
Consolidated learning -- a domain-specific model-free optimization strategy with examples for XGBoost and MIMIC-IVCode1
Continued Pretraining for Better Zero- and Few-Shot PromptabilityCode1
Harnessing Meta-Learning for Improving Full-Frame Video StabilizationCode1
HELP: Hardware-Adaptive Efficient Latency Prediction for NAS via Meta-LearningCode1
Context-Aware Meta-LearningCode1
How to Retrain Recommender System? A Sequential Meta-Learning MethodCode1
How to Train Your MAML to Excel in Few-Shot ClassificationCode1
How to trust unlabeled data? Instance Credibility Inference for Few-Shot LearningCode1
A Channel Coding Benchmark for Meta-LearningCode1
Continuous Optical Zooming: A Benchmark for Arbitrary-Scale Image Super-Resolution in Real WorldCode1
Copolymer Informatics with Multi-Task Deep Neural NetworksCode1
An Accurate and Fully-Automated Ensemble Model for Weekly Time Series ForecastingCode1
A Structured Dictionary Perspective on Implicit Neural RepresentationsCode1
Adaptive-Control-Oriented Meta-Learning for Nonlinear SystemsCode1
Contrastive Meta Learning with Behavior Multiplicity for RecommendationCode1
ContrastNet: A Contrastive Learning Framework for Few-Shot Text ClassificationCode1
Cross-Domain Few-Shot Classification via Adversarial Task AugmentationCode1
Improving Language Plasticity via Pretraining with Active ForgettingCode1
Incremental Few-Shot Object Detection via Simple Fine-Tuning ApproachCode1
Covariate Distribution Aware Meta-learningCode1
AirDet: Few-Shot Detection without Fine-tuning for Autonomous ExplorationCode1
Cross-Domain Few-Shot Semantic SegmentationCode1
Cross-domain Few-shot Object Detection with Multi-modal Textual EnrichmentCode1
Fast and Efficient Local Search for Genetic Programming Based Loss Function LearningCode1
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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