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 2551–2600 of 3569 papers

TitleStatusHype
CoMeta: Enhancing Meta Embeddings with Collaborative Information in Cold-start Problem of Recommendation—0
Communication-Efficient and Privacy-Preserving Decentralized Meta-Learning—0
Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark—0
CORL: Compositional Representation Learning for Few-Shot Classification—0
Complementary Meta-Reinforcement Learning for Fault-Adaptive Control—0
Complex Emotion Recognition System using basic emotions via Facial Expression, EEG, and ECG Signals: a review—0
Compositional ADAM: An Adaptive Compositional Solver—0
Compositional federated learning: Applications in distributionally robust averaging and meta learning—0
Compositional learning of functions in humans and machines—0
CoMPS: Continual Meta Policy Search—0
Concept Discovery for Fast Adapatation—0
Conditional Meta-Learning of Linear Representations—0
Conditional Mutual Information-Based Generalization Bound for Meta Learning—0
Conditional Super Learner—0
ConFeSS: A Framework for Single Source Cross-Domain Few-Shot Learning—0
Confounder Identification-free Causal Visual Feature Learning—0
Confusable Learning for Large-class Few-Shot Classification—0
ConML: A Universal Meta-Learning Framework with Task-Level Contrastive Learning—0
Connection-Adaptive Meta-Learning—0
Constrained Few-Shot Learning: Human-Like Low Sample Complexity Learning and Non-Episodic Text Classification—0
Constrained Meta Agnostic Reinforcement Learning—0
Constructing a meta-learner for unsupervised anomaly detection—0
Context-Aware Safe Reinforcement Learning for Non-Stationary Environments—0
Context-aware Visual Tracking with Joint Meta-updating—0
Context-Conditional Navigation with a Learning-Based Terrain- and Robot-Aware Dynamics Model—0
Context-Conditioned Spatio-Temporal Predictive Learning for Reliable V2V Channel Prediction—0
Connecting Context-specific Adaptation in Humans to Meta-learning—0
Contextual HyperNetworks for Novel Feature Adaptation—0
Contextual Stochastic Bilevel Optimization—0
Continual Few-Shot Learning with Adversarial Class Storage—0
Continual learning under domain transfer with sparse synaptic bursting—0
Continual Quality Estimation with Online Bayesian Meta-Learning—0
Continuous Learning in a Hierarchical Multiscale Neural Network—0
Continuous-Time Meta-Learning with Forward Mode Differentiation—0
Contrastive Conditional Neural Processes—0
Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification—0
Contrastive Learning is Just Meta-Learning—0
Contrastive Prototype Learning with Augmented Embeddings for Few-Shot Learning—0
Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning—0
Convergence-aware Clustered Federated Graph Learning Framework for Collaborative Inter-company Labor Market Forecasting—0
Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes—0
Convergence of Gradient-based MAML in LQR—0
Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters—0
Convergence Properties of Stochastic Hypergradients—0
MetaCVR: Conversion Rate Prediction via Meta Learning in Small-Scale Recommendation Scenarios—0
Convolutional Neural Networks Can (Meta-)Learn the Same-Different Relation—0
Convolutional Neural Processes for Inpainting Satellite Images—0
Coordinated Control of Deformation and Flight for Morphing Aircraft via Meta-Learning and Coupled State-Dependent Riccati Equations—0
Correction Networks: Meta-Learning for Zero-Shot Learning—0
CosmosDSR -- a methodology for automated detection and tracking of orbital debris using the Unscented Kalman Filter—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