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 31713180 of 3569 papers

TitleStatusHype
Meta-learning Amidst Heterogeneity and AmbiguityCode0
Meta-learning and Data Augmentation for Stress Testing Forecasting ModelsCode0
Dynamic Loss For Robust LearningCode0
Iceberg: Enhancing HLS Modeling with Synthetic DataCode0
Dynamic Kernel Selection for Improved Generalization and Memory Efficiency in Meta-learningCode0
Meta-Learning and Self-Supervised Pretraining for Real World Image TranslationCode0
Dual Meta-Learning with Longitudinally Generalized Regularization for One-Shot Brain Tissue Segmentation Across the Human LifespanCode0
Meta-Learning an Evolvable Developmental EncodingCode0
Dual-level Mixup for Graph Few-shot Learning with Fewer TasksCode0
Neuronal diversity can improve machine learning for physics and beyondCode0
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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