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
Early Warning Prediction with Automatic Labeling in Epilepsy Patients—0
Bayesian Meta-Learning on Control Barrier Functions with Data from On-Board Sensors—0
A Meta-learning based Distribution System Load Forecasting Model Selection Framework—0
Early-Stopping for Meta-Learning: Estimating Generalization from the Activation Dynamics—0
Bayesian meta learning for trustworthy uncertainty quantification—0
Dynamic Task Weighting Methods for Multi-task Networks in Autonomous Driving Systems—0
Dynamic Spectral Backpropagation for Efficient Neural Network Training—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
Adaptive Optimization Algorithms for Machine Learning—0
Dynamics of Meta-learning Representation in the Teacher-student Scenario—0
Bayesian Meta-Learning for Few-Shot 3D Shape Completion—0
Dynamic Regret Analysis for Online Meta-Learning—0
Dynamic population-based meta-learning for multi-agent communication with natural language—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
Dynamic Memory Induction Networks for Few-Shot Text Classification—0
Bayesian-Boosted MetaLoc: Efficient Training and Guaranteed Generalization for Indoor Localization—0
Dynamic Link Prediction for New Nodes in Temporal Graph Networks—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 Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends—0
A Better Baseline for Second Order Gradient Estimation in Stochastic Computation Graphs—0
Dynamic Learning Rate for Deep Reinforcement Learning: A Bandit Approach—0
Dynamic Knowledge Graph-based Dialogue Generation with Improved Adversarial Meta-Learning—0
Show:102550
← PrevPage 64 of 143Next →
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