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

Showing 12511275 of 3569 papers

TitleStatusHype
Toward Unsupervised Outlier Model SelectionCode1
Deep meta-learning for the selection of accurate ultrasound based breast mass classifier0
Meta-PDE: Learning to Solve PDEs Quickly Without a Mesh0
Faster Adaptive Momentum-Based Federated Methods for Distributed Composition Optimization0
HyperSound: Generating Implicit Neural Representations of Audio Signals with Hypernetworks0
Fast Adaptive Federated Bilevel Optimization0
Deep Multimodal Fusion for Generalizable Person Re-identificationCode0
A Meta-GNN approach to personalized seizure detection and classification0
Meta-Learning for Unsupervised Outlier Detection with Optimal Transport0
Data-Efficient Cross-Lingual Transfer with Language-Specific Subnetworks0
Optimizing Closed-Loop Performance with Data from Similar Systems: A Bayesian Meta-Learning Approach0
A new benchmark for group distribution shifts in hand grasp regression for object manipulation. Can meta-learning raise the bar?Code0
Learning New Tasks from a Few Examples with Soft-Label PrototypesCode0
A picture of the space of typical learnable tasksCode1
Few-Shot Classification of Skin Lesions from Dermoscopic Images by Meta-Learning Representative EmbeddingsCode0
Meta-Learning Biologically Plausible Plasticity Rules with Random Feedback PathwaysCode1
SEMPAI: a Self-Enhancing Multi-Photon Artificial Intelligence for prior-informed assessment of muscle function and pathology0
RESUS: Warm-Up Cold Users via Meta-Learning Residual User Preferences in CTR PredictionCode0
Meta-Learning Initializations for Interactive Medical Image Registration0
Imputation of missing values in multi-view data0
Multi-Environment based Meta-Learning with CSI Fingerprints for Radio Based Positioning0
Uncertainty-based Meta-Reinforcement Learning for Robust Radar Tracking0
Which is the best model for my data?0
UTILIZING FEDERATED LEARNING AND META LEARNING FOR FEW-SHOT LEARNING ON EDGE DEVICES0
Leveraging Open Data and Task Augmentation to Automated Behavioral Coding of Psychotherapy Conversations in Low-Resource Scenarios0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified