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

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Showing 2061–2070 of 3569 papers

TitleStatusHype
Non-stationary Bandits and Meta-Learning with a Small Set of Optimal ArmsCode0
Towards Better Meta-Initialization with Task Augmentation for Kindergarten-aged Speech Recognition—0
Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications—0
Reinforcement Learning in Practice: Opportunities and Challenges—0
Towards Tailored Models on Private AIoT Devices: Federated Direct Neural Architecture Search—0
Enabling Reproducibility and Meta-learning Through a Lifelong Database of Experiments (LDE)—0
Imbalanced Classification via Explicit Gradient Learning From Augmented Data—0
A History of Meta-gradient: Gradient Methods for Meta-learning—0
Trace norm regularization for multi-task learning with scarce dataCode0
Meta-learning with GANs for anomaly detection, with deployment in high-speed rail inspection system—0
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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