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 28912900 of 3569 papers

TitleStatusHype
Concept-free Causal Disentanglement with Variational Graph Auto-EncoderCode0
Writer adaptation for offline text recognition: An exploration of neural network-based methodsCode0
Learning vs Retrieval: The Role of In-Context Examples in Regression with LLMsCode0
Learning What and Where to TransferCode0
Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement LearningCode0
Learning Where to Edit Vision TransformersCode0
Meta Temporal Point ProcessesCode0
Learning to Design RNACode0
Learning to Demodulate from Few Pilots via Offline and Online Meta-LearningCode0
Improving Few-Shot Learning through Multi-task Representation Learning TheoryCode0
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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