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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1051–1100 of 3569 papers

TitleStatusHype
Learning to Customize Model Structures for Few-shot Dialogue Generation TasksCode0
Learning to Design RNACode0
Learning to Evolve on Dynamic GraphsCode0
Cost Adaptation for Robust Decentralized Swarm BehaviourCode0
A Scalable AutoML Approach Based on Graph Neural NetworksCode0
Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution TasksCode0
Learning to adapt: a meta-learning approach for speaker adaptationCode0
Cooperative Meta-Learning with Gradient AugmentationCode0
Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learningCode0
Learning to Continually Learn Rapidly from Few and Noisy DataCode0
Learning to Learn Kernels with Variational Random FeaturesCode0
Learning Low-Dimensional Embeddings for Black-Box OptimizationCode0
Learning How to Demodulate from Few Pilots via Meta-LearningCode0
Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce ScenariosCode0
Learning Invariances for Policy GeneralizationCode0
Learning New Tasks from a Few Examples with Soft-Label PrototypesCode0
Learning Generalized Zero-Shot Learners for Open-Domain Image GeolocalizationCode0
Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot CoordinationCode0
Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal PredictionCode0
Learning Deep Morphological Networks with Neural Architecture SearchCode0
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
A Representation Learning Perspective on the Importance of Train-Validation Splitting in Meta-LearningCode0
Learning an Explicit Hyperparameter Prediction Function Conditioned on TasksCode0
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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All datasetsML10MT50ML45OMNIGLOT - 1-Shot, 20-way

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8—Unverified
2MZMeta-train success rate97.6—Unverified
3MAMLMeta-test success rate36—Unverified
4RL^2Meta-test success rate10—Unverified
5DnCMeta-test success rate5.4—Unverified
6PEARLMeta-test success rate0—Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60—Unverified
2Multi-task multi-head SACAverage Success Rate35.85—Unverified
3DisCorAverage Success Rate26—Unverified
4NDPAverage Success Rate11—Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5—Unverified
2MZMeta-test success rate (zero-shot)17.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75—Unverified