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 1–10 of 3569 papers

TitleStatusHype
A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and ApplicationsCode9
Darwin Godel Machine: Open-Ended Evolution of Self-Improving AgentsCode5
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a SecondCode5
Secrets of RLHF in Large Language Models Part II: Reward ModelingCode5
RecBole 2.0: Towards a More Up-to-Date Recommendation LibraryCode4
Auto-Sklearn 2.0: Hands-free AutoML via Meta-LearningCode3
Discovered Policy OptimisationCode3
Adversarial Cheap TalkCode3
ROLAND: Graph Learning Framework for Dynamic GraphsCode3
DevFormer: A Symmetric Transformer for Context-Aware Device PlacementCode2
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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