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 1911–1920 of 3569 papers

TitleStatusHype
Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks—0
Meta-learning for Out-of-Distribution Detection via Density Estimation in Latent Space—0
EEML: Ensemble Embedded Meta-learning—0
Motley: Benchmarking Heterogeneity and Personalization in Federated LearningCode0
MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-LearningCode0
Provable Generalization of Overparameterized Meta-learning Trained with SGD—0
Fast Finite Width Neural Tangent KernelCode0
Principled Acceleration of Iterative Numerical Methods Using Machine Learning—0
On Enforcing Better Conditioned Meta-Learning for Rapid Few-Shot Adaptation—0
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification—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