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 851–875 of 3569 papers

TitleStatusHype
A Meta-Learning Approach to One-Step Active Learning—0
Dual Meta-Learning with Longitudinally Consistent Regularization for One-Shot Brain Tissue Segmentation Across the Human Lifespan—0
Dual Path Structural Contrastive Embeddings for Learning Novel Objects—0
DyGSSM: Multi-view Dynamic Graph Embeddings with State Space Model Gradient Update—0
Fast & Slow Learning: Incorporating Synthetic Gradients in Neural Memory Controllers—0
Dynamic Channel Access via Meta-Reinforcement Learning—0
Fast Training of Neural Lumigraph Representations using Meta Learning—0
Dynamic Environment Responsive Online Meta-Learning with Fairness Awareness—0
Dynamic Indoor Fingerprinting Localization based on Few-Shot Meta-Learning with CSI Images—0
A Meta-Learning Approach to Predicting Performance and Data Requirements—0
Dynamic Knowledge Graph-based Dialogue Generation with Improved Adversarial Meta-Learning—0
Dynamic Learning Rate for Deep Reinforcement Learning: A Bandit Approach—0
Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Data—0
Doing More with Less: Overcoming Data Scarcity for POI Recommendation via Cross-Region Transfer—0
Dynamic Memory Induction Networks for Few-Shot Text Classification—0
Bayesian-Boosted MetaLoc: Efficient Training and Guaranteed Generalization for Indoor Localization—0
Dynamic population-based meta-learning for multi-agent communication with natural language—0
Dynamic Regret Analysis for Online Meta-Learning—0
Does Meta-learning Help mBERT for Few-shot Question Generation in a Cross-lingual Transfer Setting for Indic Languages?—0
Dynamics of Meta-learning Representation in the Teacher-student Scenario—0
Dynamic Spectral Backpropagation for Efficient Neural Network Training—0
Dynamic Task Weighting Methods for Multi-task Networks in Autonomous Driving Systems—0
A Meta-Learning Algorithm for Interrogative Agendas—0
Early-Stopping for Meta-Learning: Estimating Generalization from the Activation Dynamics—0
AutoMLBench: A Comprehensive Experimental Evaluation of Automated Machine Learning Frameworks—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