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
Is Meta-training Really Necessary for Molecular Few-Shot Learning ?—0
Is Pre-training Truly Better Than Meta-Learning?—0
How well does your sampler really work?—0
Customized Conversational Recommender Systems—0
Joint autoencoders: a flexible meta-learning framework—0
Hybrid Consistency Training with Prototype Adaptation for Few-Shot Learning—0
Generalized Face Anti-Spoofing via Multi-Task Learning and One-Side Meta Triplet Loss—0
Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles—0
Contrastive Conditional Neural Processes—0
Hybrid-Task Meta-Learning: A Graph Neural Network Approach for Scalable and Transferable Bandwidth Allocation—0
Hyperbolic Dual Feature Augmentation for Open-Environment—0
Hyperbolic Graph Neural Networks at Scale: A Meta Learning Approach—0
Continuous-Time Meta-Learning with Forward Mode Differentiation—0
HyPer-EP: Meta-Learning Hybrid Personalized Models for Cardiac Electrophysiology—0
HyperLoRA for PDEs—0
Generalized Cross-domain Multi-label Few-shot Learning for Chest X-rays—0
Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Tasks—0
Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks—0
Generalization in Neural Networks: A Broad Survey—0
Invenio: Discovering Hidden Relationships Between Tasks/Domains Using Structured Meta Learning—0
Generalizable speech deepfake detection via meta-learned LoRA—0
A General framework for PAC-Bayes Bounds for Meta-Learning—0
Hypernymy Detection for Low-Resource Languages via Meta Learning—0
Adapting to Dynamic LEO-B5G Systems: Meta-Critic Learning Based Efficient Resource Scheduling—0
Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond—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