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 2551–2575 of 3569 papers

TitleStatusHype
Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms—0
Task Aligned Generative Meta-learning for Zero-shot Learning—0
Domain Generalization: A Survey—0
Meta-Learning with Variational BayesCode0
Meta-Learning-Based Robust Adaptive Flight Control Under Uncertain Wind Conditions—0
A Brief Summary of Interactions Between Meta-Learning and Self-Supervised Learning—0
Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection—0
Meta-learning representations for clustering with infinite Gaussian mixture models—0
Meta-Learning an Inference Algorithm for Probabilistic Programs—0
Meta-Learning with Graph Neural Networks: Methods and Applications—0
Meta-Learning for improving rare word recognition in end-to-end ASR—0
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning—0
Multi-Domain Learning by Meta-Learning: Taking Optimal Steps in Multi-Domain Loss Landscapes by Inner-Loop Learning—0
Credit Assignment with Meta-Policy Gradient for Multi-Agent Reinforcement Learning—0
Self-Domain Adaptation for Face Anti-Spoofing—0
Trajectory-Based Meta-Learning for Out-Of-Vocabulary Word Embedding Learning—0
Identifying Physical Law of Hamiltonian Systems via Meta-Learning—0
Two Sides of Meta-Learning Evaluation: In vs. Out of DistributionCode0
Meta-Learned Attribute Self-Gating for Continual Generalized Zero-Shot Learning—0
Domain Adaptation in Dialogue Systems using Transfer and Meta-Learning—0
Provably Improved Context-Based Offline Meta-RL with Attention and Contrastive Learning—0
Unsupervised Meta Learning for One Shot Title Compression in Voice Commerce—0
Fast On-Device Adaptation for Spiking Neural Networks via Online-Within-Online Meta-Learning—0
Robust and Differentially Private Mean EstimationCode0
Group Equivariant Conditional Neural Processes—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