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 2151–2175 of 3569 papers

TitleStatusHype
Noether Networks: Meta-Learning Useful Conserved Quantities—0
A Multi-Strategy based Pre-Training Method for Cold-Start Recommendation—0
Self-supervised Graph Learning for Occasional Group Recommendation—0
Fast Data-Driven Adaptation of Radar Detection via Meta-Learning—0
Meta Arcade: A Configurable Environment Suite for Meta-Learning—0
Automatic Unsupervised Outlier Model Selection—0
Learning to Learn Dense Gaussian Processes for Few-Shot Learning—0
Fast Training Method for Stochastic Compositional Optimization Problems—0
Meta-Learning via Learning with Distributed Memory—0
Learning to Adapt via Latent Domains for Adaptive Semantic Segmentation—0
Effective Meta-Regularization by Kernelized Proximal Regularization—0
On the Practical Consistency of Meta-Reinforcement Learning Algorithms—0
Generative vs. Discriminative: Rethinking The Meta-Continual LearningCode0
Functionally Regionalized Knowledge Transfer for Low-resource Drug Discovery—0
Variational Continual Bayesian Meta-Learning—0
MAMRL: Exploiting Multi-agent Meta Reinforcement Learning in WAN Traffic Engineering—0
Leveraging The Topological Consistencies of Learning in Deep Neural Networks—0
Confounder Identification-free Causal Visual Feature Learning—0
A Close Look at Few-shot Real Image Super-resolution from the Distortion Relation Perspective—0
Joint inference and input optimization in equilibrium networksCode0
Meta Mask Correction for Nuclei Segmentation in Histopathological Image—0
Exploration of Dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology—0
Sharing to learn and learning to share; Fitting together Meta-Learning, Multi-Task Learning, and Transfer Learning: A meta review—0
Meta-learning richer priors for VAEs—0
Reinforcement Learning for Few-Shot Text Generation AdaptationCode0
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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