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 776–800 of 3569 papers

TitleStatusHype
Adaptive Optimization Algorithms for Machine Learning—0
Bayesian meta learning for trustworthy uncertainty quantification—0
Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms—0
A Meta-Learning Approach to the Optimal Power Flow Problem Under Topology Reconfigurations—0
A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends—0
Bayesian Meta-Learning for Few-Shot 3D Shape Completion—0
Bayesian decision-making under misspecified priors with applications to meta-learning—0
A Meta-learning Approach to Reservoir Computing: Time Series Prediction with Limited Data—0
Bayesian-Boosted MetaLoc: Efficient Training and Guaranteed Generalization for Indoor Localization—0
Bayesian and Multi-Armed Contextual Meta-Optimization for Efficient Wireless Radio Resource Management—0
A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network—0
A Better Baseline for Second Order Gradient Estimation in Stochastic Computation Graphs—0
Exploring Domain Shift in Extractive Text Summarization—0
BatMan-CLR: Making Few-shots Meta-Learners Resilient Against Label Noise—0
A Meta-Learning Approach to Predicting Performance and Data Requirements—0
Adaptive Hierarchical Hyper-gradient Descent—0
Bayesian Active Meta-Learning for Black-Box Optimization—0
A Meta-Learning Approach to Population-Based Modelling of Structures—0
Diversity-boosted Generalization-Specialization Balancing for Zero-shot Learning—0
A Meta-Learning Approach to One-Step Active Learning—0
A Comprehensive Review of Few-shot Action Recognition—0
A Meta Learning Approach to Grammatical Error Correction—0
Adaptive Mixture of Experts Learning for Generalizable Face Anti-Spoofing—0
Exploiting Style Transfer-based Task Augmentation for Cross-Domain Few-Shot Learning—0
Backdoor Attacks on Federated Meta-Learning—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