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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1271–1280 of 3569 papers

TitleStatusHype
Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions—0
Improved Relation Networks for End-to-End Speaker Verification and Identification—0
Global Perception Based Autoregressive Neural Processes—0
When Does MAML Objective Have Benign Landscape?—0
Convergence Properties of Stochastic Hypergradients—0
Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters—0
Convolutional Neural Networks Can (Meta-)Learn the Same-Different Relation—0
A Generic First-Order Algorithmic Framework for Bi-Level Programming Beyond Lower-Level Singleton—0
Improve Noise Tolerance of Robust Loss via Noise-Awareness—0
Convergence of Gradient-based MAML in LQR—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