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 626650 of 3569 papers

TitleStatusHype
Exploration in Approximate Hyper-State Space for Meta Reinforcement LearningCode1
Contrastive Meta Learning with Behavior Multiplicity for RecommendationCode1
Learning with AMIGo: Adversarially Motivated Intrinsic GoalsCode1
ContrastNet: A Contrastive Learning Framework for Few-Shot Text ClassificationCode1
Control-oriented meta-learningCode1
A General Descent Aggregation Framework for Gradient-based Bi-level OptimizationCode1
LiST: Lite Prompted Self-training Makes Parameter-Efficient Few-shot LearnersCode1
ArtFID: Quantitative Evaluation of Neural Style TransferCode1
LoRA Recycle: Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAsCode1
MAMO: Memory-Augmented Meta-Optimization for Cold-start RecommendationCode1
Many-Class Few-Shot Learning on Multi-Granularity Class HierarchyCode1
Massive Editing for Large Language Models via Meta LearningCode1
MC-BERT: Efficient Language Pre-Training via a Meta ControllerCode1
Few-shot Object Detection via Feature ReweightingCode1
Deep Random Projector: Accelerated Deep Image PriorCode1
Meta Adversarial Training against Universal PatchesCode1
Copolymer Informatics with Multi-Task Deep Neural NetworksCode1
MetaAudio: A Few-Shot Audio Classification BenchmarkCode1
MetaAvatar: Learning Animatable Clothed Human Models from Few Depth ImagesCode1
HELP: Hardware-Adaptive Efficient Latency Prediction for NAS via Meta-LearningCode1
Meta-Calibration: Learning of Model Calibration Using Differentiable Expected Calibration ErrorCode1
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few ExamplesCode1
Covariate Distribution Aware Meta-learningCode1
MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta LearningCode1
MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal ControlCode1
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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