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 1701–1725 of 3569 papers

TitleStatusHype
M2I2: Learning Efficient Multi-Agent Communication via Masked State Modeling and Intention Inference—0
Dual-Level Viewpoint-Learning for Cross-Domain Vehicle Re-Identification—0
MAC: A Meta-Learning Approach for Feature Learning and Recombination—0
Machine Learning Approaches For Motor Learning: A Short Review—0
Learning to Navigate the Web—0
Learning to Optimise General TSP Instances—0
MADOD: Generalizing OOD Detection to Unseen Domains via G-Invariance Meta-Learning—0
Learning to Optimize on SPD Manifolds—0
Learning to Profile: User Meta-Profile Network for Few-Shot Learning—0
DyGSSM: Multi-view Dynamic Graph Embeddings with State Space Model Gradient Update—0
Constructing a meta-learner for unsupervised anomaly detection—0
Forecast with Forecasts: Diversity Matters—0
Dynamic Channel Access via Meta-Reinforcement Learning—0
Low-Resource Multilingual and Zero-Shot Multispeaker TTS—0
Forecasting Market Prices using DL with Data Augmentation and Meta-learning: ARIMA still wins!—0
Learning to Recover from Failures using Memory—0
Dynamic Environment Responsive Online Meta-Learning with Fairness Awareness—0
Bayesian Active Meta-Learning for Black-Box Optimization—0
Learning to Reinforcement Learn by Imitation—0
Dynamic Indoor Fingerprinting Localization based on Few-Shot Meta-Learning with CSI Images—0
Learning to Remember from a Multi-Task Teacher—0
Learning to Retain while Acquiring: Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation—0
A Feature Subset Selection Algorithm Automatic Recommendation Method—0
Learning to Sample: an Active Learning Framework—0
Adapting Mental Health Prediction Tasks for Cross-lingual Learning via Meta-Training and In-context Learning with Large Language Model—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