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 10761100 of 3569 papers

TitleStatusHype
Learning Fast Adaptation with Meta Strategy OptimizationCode0
Learning One-Shot Imitation from Humans without HumansCode0
learn2learn: A Library for Meta-Learning ResearchCode0
Continuous Meta-Learning without TasksCode0
Are LSTMs Good Few-Shot Learners?Code0
An Ensemble of Epoch-wise Empirical Bayes for Few-shot LearningCode0
Continuous Adaptation via Meta-Learning in Nonstationary and Competitive EnvironmentsCode0
Layer-compensated Pruning for Resource-constrained Convolutional Neural NetworksCode0
Leaping Through Time with Gradient-based Adaptation for RecommendationCode0
Latent Bottlenecked Attentive Neural ProcessesCode0
Latent-Optimized Adversarial Neural Transfer for Sarcasm DetectionCode0
Continual Adaptation of Visual Representations via Domain Randomization and Meta-learningCode0
Latent Representation Learning of Multi-scale Thermophysics: Application to Dynamics in Shocked Porous Energetic MaterialCode0
Contextualizing Meta-Learning via Learning to DecomposeCode0
Contextualizing Enhances Gradient Based Meta LearningCode0
Contextual Gradient Scaling for Few-Shot LearningCode0
Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and RecognitionCode0
LabelCraft: Empowering Short Video Recommendations with Automated Label CraftingCode0
Latent Task-Specific Graph Network SimulatorsCode0
Learning advisor networks for noisy image classificationCode0
Joint inference and input optimization in equilibrium networksCode0
It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density EstimationCode0
Arbitrary Scale Super-Resolution for Brain MRI ImagesCode0
Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in SegmentationCode0
Is Bayesian Model-Agnostic Meta Learning Better than Model-Agnostic Meta Learning, Provably?Code0
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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