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 951975 of 3569 papers

TitleStatusHype
Virtual Node Tuning for Few-shot Node Classification0
In-Context Learning through the Bayesian PrismCode0
EMO: Episodic Memory Optimization for Few-Shot Meta-Learning0
Meta-Learning in Spiking Neural Networks with Reward-Modulated STDP0
Decentralized Multi-Level Compositional Optimization Algorithms with Level-Independent Convergence Rate0
GSHOT: Few-shot Generative Modeling of Labeled GraphsCode0
Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial OptimizationCode1
Evolution of Efficient Symbolic Communication Codes0
Multi-Predict: Few Shot Predictors For Efficient Neural Architecture Search0
A Generalized Alternating Method for Bilevel Learning under the Polyak-Łojasiewicz Condition0
TART: Improved Few-shot Text Classification Using Task-Adaptive Reference TransformationCode0
Meta-Learning Framework for End-to-End Imposter Identification in Unseen Speaker Recognition0
Effective Structured Prompting by Meta-Learning and Representative VerbalizerCode0
MetaXLR -- Mixed Language Meta Representation Transformation for Low-resource Cross-lingual Learning based on Multi-Armed BanditCode0
Towards Omni-generalizable Neural Methods for Vehicle Routing ProblemsCode1
SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning0
Taylorformer: Probabilistic Modelling for Random Processes including Time SeriesCode0
Adaptive Conditional Quantile Neural ProcessesCode0
Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in SegmentationCode0
AlteredAvatar: Stylizing Dynamic 3D Avatars with Fast Style Adaptation0
Task-Equivariant Graph Few-shot LearningCode1
A Meta-learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning0
Learning to Learn from APIs: Black-Box Data-Free Meta-LearningCode1
Zero- and Few-Shot Event Detection via Prompt-Based Meta LearningCode1
Im-Promptu: In-Context Composition from Image PromptsCode0
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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