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 1651–1700 of 3569 papers

TitleStatusHype
Context-Conditioned Spatio-Temporal Predictive Learning for Reliable V2V Channel Prediction—0
A Few Shot Adaptation of Visual Navigation Skills to New Observations using Meta-Learning—0
Learning to generate imaginary tasks for improving generalization in meta-learning—0
Domain Generalization through Meta-Learning: A Survey—0
Learning to Identify Physical Laws of Hamiltonian Systems via Meta-Learning—0
Learning to Infer Counterfactuals: Meta-Learning for Estimating Multiple Imbalanced Treatment Effects—0
Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?—0
Learning to Learn a Cold-start Sequential Recommender—0
From Text to Treatment Effects: A Meta-Learning Approach to Handling Text-Based Confounding—0
Context-Conditional Navigation with a Learning-Based Terrain- and Robot-Aware Dynamics Model—0
From Biased Data to Unbiased Models: a Meta-Learning Approach—0
From Abstraction to Reality: DARPA's Vision for Robust Sim-to-Real Autonomy—0
Context-aware Visual Tracking with Joint Meta-updating—0
A First Order Meta Stackelberg Method for Robust Federated Learning—0
Long Short-Term Temporal Meta-learning in Online Recommendation—0
Learning To Learn by Jointly Optimizing Neural Architecture and Weights—0
Don’t Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights—0
Learning to Learn Dense Gaussian Processes for Few-Shot Learning—0
Context-Aware Safe Reinforcement Learning for Non-Stationary Environments—0
Learning to Learn End-to-End Goal-Oriented Dialog From Related Dialog Tasks—0
FRANS: Automatic Feature Extraction for Time Series Forecasting—0
Arbitrary Order Meta-Learning with Simple Population-Based Evolution—0
Double Meta-Learning for Data Efficient Policy Optimization in Non-Stationary Environments—0
Learning to Learn Group Alignment: A Self-Tuning Credo Framework with Multiagent Teams—0
Foundations of Cyber Resilience: The Confluence of Game, Control, and Learning Theories—0
LogAnMeta: Log Anomaly Detection Using Meta Learning—0
A Pseudo-Label Method for Coarse-to-Fine Multi-Label Learning with Limited Supervision—0
Learning to Learn: Meta-Critic Networks for Sample Efficient Learning—0
Learning to Learn Morphological Inflection for Resource-Poor Languages—0
Learning to Learn Neural Networks—0
FORML: Learning to Reweight Data for Fairness—0
Learning-to-Learn Personalised Human Activity Recognition Models—0
Learning to Learn Semantic Factors in Heterogeneous Image Classification—0
DreamPRM: Domain-Reweighted Process Reward Model for Multimodal Reasoning—0
Learning-to-Learn the Wave Angle Estimation—0
Learning to Learn to be Right for the Right Reasons—0
Localized Meta-Learning: A PAC-Bayes Analysis for Meta-Leanring Beyond Global Prior—0
Learning to learn to communicate—0
FORML: A Riemannian Hessian-free Method for Meta-learning on Stiefel Manifolds—0
Localized Meta-Learning: A PAC-Bayes Analysis for Meta-Learning Beyond Global Prior—0
Learning to Learn Transferable Generative Attack for Person Re-Identification—0
Learning to Learn Unlearned Feature for Brain Tumor Segmentation—0
DSDRNet: Disentangling Representation and Reconstruct Network for Domain Generalization—0
Learning to Learn Weight Generation via Local Consistency Diffusion—0
Local transfer learning from one data space to another—0
LogitMat : Zeroshot Learning Algorithm for Recommender Systems without Transfer Learning or Pretrained Models—0
Learning to Learn with Indispensable Connections—0
Constructing a meta-learner for unsupervised anomaly detection—0
Forecast with Forecasts: Diversity Matters—0
LiMAML: Personalization of Deep Recommender Models via Meta Learning—0
Show:102550
← PrevPage 34 of 72Next →
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