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 1441–1450 of 3569 papers

TitleStatusHype
Nonlinear Meta-Learning Can Guarantee Faster Rates—0
Fast Unsupervised Deep Outlier Model Selection with HypernetworksCode0
Towards a population-informed approach to the definition of data-driven models for structural dynamics—0
Forecasting Early with Meta LearningCode0
Exploiting Field Dependencies for Learning on Categorical DataCode0
Context-Conditional Navigation with a Learning-Based Terrain- and Robot-Aware Dynamics Model—0
A Meta-Learning Based Precoder Optimization Framework for Rate-Splitting Multiple Access—0
TinyMetaFed: Efficient Federated Meta-Learning for TinyML—0
Hybrid Control Policy for Artificial Pancreas via Ensemble Deep Reinforcement Learning—0
Generalizing Supervised Deep Learning MRI Reconstruction to Multiple and Unseen Contrasts using Meta-Learning HypernetworksCode0
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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