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 501–525 of 3569 papers

TitleStatusHype
FADE: Towards Fairness-aware Augmentation for Domain Generalization via Classifier-Guided Score-based Diffusion Models—0
Meta-Learning an Evolvable Developmental EncodingCode0
Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo SimulationCode1
Noise-Aware Differentially Private Regression via Meta-LearningCode0
Multi-Teacher Multi-Objective Meta-Learning for Zero-Shot Hyperspectral Band Selection—0
Meta-Learning Neural Procedural Biases—0
Fairness-Aware Meta-Learning via Nash Bargaining—0
Agnostic Sharpness-Aware Minimization—0
Meta Learning Text-to-Speech Synthesis in over 7000 Languages—0
Few-Shot Load Forecasting Under Data Scarcity in Smart Grids: A Meta-Learning Approach—0
Cooperative Meta-Learning with Gradient AugmentationCode0
Graph Mining under Data scarcity—0
Meta-learning for Positive-unlabeled Classification—0
What Do Language Models Learn in Context? The Structured Task HypothesisCode1
Privacy Challenges in Meta-Learning: An Investigation on Model-Agnostic Meta-Learning—0
GS-Phong: Meta-Learned 3D Gaussians for Relightable Novel View SynthesisCode1
On the Limits of Multi-modal Meta-Learning with Auxiliary Task Modulation Using Conditional Batch Normalization—0
Learning to Continually Learn with the Bayesian PrincipleCode1
HarmoDT: Harmony Multi-Task Decision Transformer for Offline Reinforcement LearningCode1
Towards robust prediction of material properties for nuclear reactor design under scarce data -- a study in creep rupture property—0
Model-Agnostic Zeroth-Order Policy Optimization for Meta-Learning of Ergodic Linear Quadratic Regulators—0
Task Groupings Regularization: Data-Free Meta-Learning with Heterogeneous Pre-trained ModelsCode0
A CMDP-within-online framework for Meta-Safe Reinforcement Learning—0
Unsupervised Meta-Learning via In-Context Learning—0
Knowledge-enhanced Relation Graph and Task Sampling for Few-shot Molecular Property PredictionCode0
Show:102550
← PrevPage 21 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