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 401410 of 3569 papers

TitleStatusHype
Learning to Learn Transferable Generative Attack for Person Re-Identification0
Learning vs Retrieval: The Role of In-Context Examples in Regression with LLMsCode0
WarpAdam: A new Adam optimizer based on Meta-Learning approach0
A New First-Order Meta-Learning Algorithm with Convergence Guarantees0
Transfer-based Adversarial Poisoning Attacks for Online (MIMO-)Deep Receviers0
Few-shot Multi-Task Learning of Linear Invariant Features with Meta Subspace Pursuit0
Boosting Generalizability towards Zero-Shot Cross-Dataset Single-Image Indoor Depth by Meta-Initialization0
Real-Time Machine Learning Strategies for a New Kind of Neuroscience Experiments0
Meta-Learning Empowered Graph Neural Networks for Radio Resource Management0
LeMON: Learning to Learn Multi-Operator NetworksCode0
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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