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 1576–1600 of 3569 papers

TitleStatusHype
Learning from Noisy Demonstration Sets via Meta-Learned Suitability Assessor—0
Learning to Tune XGBoost with XGBoost—0
FS-HGR: Few-shot Learning for Hand Gesture Recognition via ElectroMyography—0
Learning from the Past: Continual Meta-Learning via Bayesian Graph Modeling—0
A Survey on Machine Learning from Few Samples—0
Learning Functional Priors and Posteriors from Data and Physics—0
DistPro: Searching A Fast Knowledge Distillation Process via Meta Optimization—0
Learning Generative Prior with Latent Space Sparsity Constraints—0
Connecting Context-specific Adaptation in Humans to Meta-learning—0
Distributed Evolution Strategies Using TPUs for Meta-Learning—0
Learning Intrinsic and Extrinsic Intentions for Cold-start Recommendation with Neural Stochastic Processes—0
Parallel Momentum Methods Under Biased Gradient Estimations—0
Learning Knowledge Representation with Meta Knowledge Distillation for Single Image Super-Resolution—0
Context-Conditioned Spatio-Temporal Predictive Learning for Reliable V2V Channel Prediction—0
Learning Low-dimensional Latent Dynamics from High-dimensional Observations: Non-asymptotics and Lower Bounds—0
Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment—0
A Few Shot Adaptation of Visual Navigation Skills to New Observations using Meta-Learning—0
Learning Modality Knowledge Alignment for Cross-Modality Transfer—0
From Text to Treatment Effects: A Meta-Learning Approach to Handling Text-Based Confounding—0
Learning Neural Processes on the Fly—0
Distributionally robust minimization in meta-learning for system identification—0
Context-Conditional Navigation with a Learning-Based Terrain- and Robot-Aware Dynamics Model—0
Learning Not to Learn: Nature versus Nurture in Silico—0
Distribution Embedding Networks for Generalization from a Diverse Set of Classification Tasks—0
From Biased Data to Unbiased Models: a Meta-Learning Approach—0
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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