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 1351–1375 of 3569 papers

TitleStatusHype
Deceptive Fairness Attacks on Graphs via Meta LearningCode0
Bayesian Active Learning in the Presence of Nuisance Parameters—0
Meta learning with language models: Challenges and opportunities in the classification of imbalanced textCode0
Are LSTMs Good Few-Shot Learners?Code0
Towards Subject Agnostic Affective Emotion Recognition—0
Meta-learning of Physics-informed Neural Networks for Efficiently Solving Newly Given PDEs—0
Solving Expensive Optimization Problems in Dynamic Environments with Meta-learningCode0
Unsupervised Representation Learning to Aid Semi-Supervised Meta LearningCode0
Are Structural Concepts Universal in Transformer Language Models? Towards Interpretable Cross-Lingual GeneralizationCode0
Bayesian Meta-Learning for Improving Generalizability of Health Prediction Models With Similar Causal MechanismsCode0
BioAct-Het: A Heterogeneous Siamese Neural Network for Bioactivity Prediction Using Novel Bioactivity RepresentatioCode0
Dynamic Link Prediction for New Nodes in Temporal Graph Networks—0
A Partially Supervised Reinforcement Learning Framework for Visual Active SearchCode0
Hypernetwork-based Meta-Learning for Low-Rank Physics-Informed Neural Networks—0
Subspace Adaptation Prior for Few-Shot LearningCode0
In-Context Learning for Few-Shot Molecular Property Prediction—0
Disentangled Latent Spaces Facilitate Data-Driven Auxiliary Learning—0
Federated Meta-Learning for Few-Shot Fault Diagnosis with Representation Encoding—0
Reset It and Forget It: Relearning Last-Layer Weights Improves Continual and Transfer Learning—0
Domain Generalization Guided by Gradient Signal to Noise Ratio of Parameters—0
Federated Multi-Level Optimization over Decentralized Networks—0
A Meta-Learning Perspective on Transformers for Causal Language Modeling—0
Early Warning Prediction with Automatic Labeling in Epilepsy Patients—0
Cost-Sensitive Best Subset Selection for Logistic Regression: A Mixed-Integer Conic Optimization Perspective—0
Understanding Transfer Learning and Gradient-Based Meta-Learning TechniquesCode0
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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