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 10511075 of 3569 papers

TitleStatusHype
Learning How to Demodulate from Few Pilots via Meta-LearningCode0
Cost Adaptation for Robust Decentralized Swarm BehaviourCode0
A Scalable AutoML Approach Based on Graph Neural NetworksCode0
Learning Deep Morphological Networks with Neural Architecture SearchCode0
Learning Fast Adaptation with Meta Strategy OptimizationCode0
Learning advisor networks for noisy image classificationCode0
Cooperative Meta-Learning with Gradient AugmentationCode0
Learning an Explicit Hyperparameter Prediction Function Conditioned on TasksCode0
Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot CoordinationCode0
Learning to Demodulate from Few Pilots via Offline and Online Meta-LearningCode0
Learning to Propagate Labels: Transductive Propagation Network for Few-shot LearningCode0
Latent Task-Specific Graph Network SimulatorsCode0
Latent Representation Learning of Multi-scale Thermophysics: Application to Dynamics in Shocked Porous Energetic MaterialCode0
Layer-compensated Pruning for Resource-constrained Convolutional Neural NetworksCode0
Latent Bottlenecked Attentive Neural ProcessesCode0
Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce ScenariosCode0
Latent-Optimized Adversarial Neural Transfer for Sarcasm DetectionCode0
An Ensemble of Epoch-wise Empirical Bayes for Few-shot LearningCode0
Knowledge-enhanced Relation Graph and Task Sampling for Few-shot Molecular Property PredictionCode0
LabelCraft: Empowering Short Video Recommendations with Automated Label CraftingCode0
Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in SegmentationCode0
Joint inference and input optimization in equilibrium networksCode0
Contrastive Meta-Learning for Few-shot Node ClassificationCode0
Are Structural Concepts Universal in Transformer Language Models? Towards Interpretable Cross-Lingual GeneralizationCode0
A Generalised Deep Meta-Learning Model for Automated Quality Control of Cardiovascular Magnetic Resonance ImagesCode0
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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