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 3401–3425 of 3569 papers

TitleStatusHype
MetaLDC: Meta Learning of Low-Dimensional Computing Classifiers for Fast On-Device Adaption—0
Meta-Learned Attribute Self-Gating for Continual Generalized Zero-Shot Learning—0
Meta-Learned Attribute Self-Interaction Network for Continual and Generalized Zero-Shot Learning—0
Meta-Learned Confidence for Transductive Few-shot Learning—0
Meta-Learned Feature Critics for Domain Generalized Semantic Segmentation—0
Meta-Learned Invariant Risk Minimization—0
Meta-Learned Per-Instance Algorithm Selection in Scholarly Recommender Systems—0
Meta-learners' learning dynamics are unlike learners'—0
Meta-Learner with Linear Nulling—0
Meta-Learning 3D Shape Segmentation Functions—0
Meta-Learning across Meta-Tasks for Few-Shot Learning—0
Meta-Learning Adaptable Foundation Models—0
Meta-Learning Adversarial Bandit Algorithms—0
Meta-Learning Adversarial Bandits—0
Meta-Learning a Dynamical Language Model—0
Meta Learning and Its Applications to Natural Language Processing—0
Meta-Learning and representation learner: A short theoretical note—0
Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm—0
Meta-Learning an In-Context Transformer Model of Human Higher Visual Cortex—0
Meta-Learning an Inference Algorithm for Probabilistic Programs—0
Meta-Learning Approaches for a One-Shot Collective-Decision Aggregation: Correctly Choosing how to Choose Correctly—0
Meta-learning approaches for few-shot learning: A survey of recent advances—0
Meta-Learning Approaches for Improving Detection of Unseen Speech Deepfakes—0
Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models—0
Meta Learning as Bayes Risk Minimization—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