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 201210 of 3569 papers

TitleStatusHype
A Low-Complexity Plug-and-Play Deep Learning Model for Massive MIMO Precoding Across Sites0
TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning0
Analytic Personalized Federated Meta-Learning0
Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts0
A Meta-learner for Heterogeneous Effects in Difference-in-Differences0
Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer0
Discovering Physics Laws of Dynamical Systems via Invariant Function Learning0
TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential RecommendationCode0
Discovering Quality-Diversity Algorithms via Meta-Black-Box Optimization0
INTACT: Inducing Noise Tolerance through Adversarial Curriculum Training for LiDAR-based Safety-Critical Perception and Autonomy0
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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