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 1126–1150 of 3569 papers

TitleStatusHype
Cross-Frequency Time Series Meta-Forecasting—0
Efficient Gradient Approximation Method for Constrained Bilevel Optimization—0
Learning to Optimize for Reinforcement LearningCode1
Meta Learning in Decentralized Neural Networks: Towards More General AI—0
Double Equivariance for Inductive Link Prediction for Both New Nodes and New Relation TypesCode0
MetaTKG: Learning Evolutionary Meta-Knowledge for Temporal Knowledge Graph Reasoning—0
Algorithm Design for Online Meta-Learning with Task Boundary Detection—0
Local transfer learning from one data space to another—0
Learning Generalized Zero-Shot Learners for Open-Domain Image GeolocalizationCode0
A Knowledge-Driven Meta-Learning Method for CSI Feedback—0
Few-Shot Object Detection via Variational Feature AggregationCode1
Contrastive Meta-Learning for Partially Observable Few-Shot LearningCode1
Online Loss Function Learning—0
Robust Meta Learning for Image based tasks—0
MetaNO: How to Transfer Your Knowledge on Learning Hidden Physics—0
Zero-shot causal learningCode0
Meta Temporal Point ProcessesCode0
Meta-Learning Mini-Batch Risk Functionals—0
Invariant Meta Learning for Out-of-Distribution Generalization—0
On the Convergence of No-Regret Learning Dynamics in Time-Varying Games—0
Rewarded meta-pruning: Meta Learning with Rewards for Channel PruningCode0
Exploiting Style Transfer-based Task Augmentation for Cross-Domain Few-Shot Learning—0
Concept Discovery for Fast Adapatation—0
Foresee What You Will Learn: Data Augmentation for Domain Generalization in Non-stationary EnvironmentCode0
Improve Noise Tolerance of Robust Loss via Noise-Awareness—0
Show:102550
← PrevPage 46 of 143Next →
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