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 1451–1475 of 3569 papers

TitleStatusHype
Writer adaptation for offline text recognition: An exploration of neural network-based methodsCode0
Advances and Challenges in Meta-Learning: A Technical Review—0
Semi Supervised Meta Learning for Spatiotemporal Learning—0
CognitiveNet: Enriching Foundation Models with Emotions and Awareness—0
MALIBO: Meta-learning for Likelihood-free Bayesian OptimizationCode0
Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms—0
LogitMat : Zeroshot Learning Algorithm for Recommender Systems without Transfer Learning or Pretrained Models—0
Meta Federated Reinforcement Learning for Distributed Resource Allocation—0
Meta-Learning Adversarial Bandit Algorithms—0
Personalized Federated Learning via Amortized Bayesian Meta-Learning—0
All in One: Multi-task Prompting for Graph Neural Networks—0
OpenClinicalAI: An Open and Dynamic Model for Alzheimer's Disease Diagnosis—0
Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learning—0
Elastically-Constrained Meta-Learner for Federated Learning—0
AutoML in Heavily Constrained ApplicationsCode0
A Meta-Learning Method for Estimation of Causal Excursion Effects to Assess Time-Varying ModerationCode0
Contrastive Meta-Learning for Few-shot Node ClassificationCode0
Near-Optimal Nonconvex-Strongly-Convex Bilevel Optimization with Fully First-Order Oracles—0
Safe Navigation in Unstructured Environments by Minimizing Uncertainty in Control and Perception—0
Is Pre-training Truly Better Than Meta-Learning?—0
Meta-Gating Framework for Fast and Continuous Resource Optimization in Dynamic Wireless Environments—0
A First Order Meta Stackelberg Method for Robust Federated Learning—0
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy ImitationCode0
SeFNet: Bridging Tabular Datasets with Semantic Feature NetsCode0
Meta-Learning for Airflow Simulations with Graph Neural Networks—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