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 3271–3280 of 3569 papers

TitleStatusHype
Designing Time-Series Models With Hypernetworks & Adversarial PortfoliosCode0
Delving into Sample Loss Curve to Embrace Noisy and Imbalanced DataCode0
Adaptive Meta-Learning for Identification of Rover-Terrain DynamicsCode0
Sample Weight Estimation Using Meta-Updates for Online Continual LearningCode0
Sampling Attacks on Meta Reinforcement Learning: A Minimax Formulation and Complexity AnalysisCode0
Task-Adaptive Meta-Learning Framework for Advancing Spatial GeneralizabilityCode0
Meta-learning based Alternating Minimization Algorithm for Non-convex OptimizationCode0
Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce ScenariosCode0
Meta-Learning for Natural Language Understanding under Continual Learning FrameworkCode0
Deep Transfer Learning Based Downlink Channel Prediction for FDD Massive MIMO SystemsCode0
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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