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 226250 of 3569 papers

TitleStatusHype
Generalizable Black-Box Adversarial Attack with Meta LearningCode1
Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo SimulationCode1
Generalizable No-Reference Image Quality Assessment via Deep Meta-learningCode1
Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive LearningCode1
Generative Meta-Learning Robust Quality-Diversity PortfolioCode1
BOME! Bilevel Optimization Made Easy: A Simple First-Order ApproachCode1
Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot LearningCode1
A Brain Graph Foundation Model: Pre-Training and Prompt-Tuning for Any Atlas and DisorderCode1
GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward non-intrusive Meta-learning of parametric PDEsCode1
Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue GenerationCode1
Graph Few-shot Class-incremental LearningCode1
Graph Meta Network for Multi-Behavior RecommendationCode1
Graph Prototypical Networks for Few-shot Learning on Attributed NetworksCode1
AReLU: Attention-based Rectified Linear UnitCode1
GS-Phong: Meta-Learned 3D Gaussians for Relightable Novel View SynthesisCode1
HarmoDT: Harmony Multi-Task Decision Transformer for Offline Reinforcement LearningCode1
Harnessing Meta-Learning for Improving Full-Frame Video StabilizationCode1
How Sensitive are Meta-Learners to Dataset Imbalance?Code1
Amortized Probabilistic Conditioning for Optimization, Simulation and InferenceCode1
Adaptive Subspaces for Few-Shot LearningCode1
How to train your MAMLCode1
A contrastive rule for meta-learningCode1
Hypernetwork approach to Bayesian MAMLCode1
Adaptive Transfer Learning on Graph Neural NetworksCode1
Bitwidth-Adaptive Quantization-Aware Neural Network Training: A Meta-Learning ApproachCode1
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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