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 17011710 of 3569 papers

TitleStatusHype
Adversarial Attacks are a Surprisingly Strong Baseline for Poisoning Few-Shot Meta-Learners0
Dynamic Loss For Robust LearningCode0
A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms0
Environmental Sensor Placement with Convolutional Gaussian Neural ProcessesCode0
Audio Anti-spoofing Using a Simple Attention Module and Joint Optimization Based on Additive Angular Margin Loss and Meta-learning0
Latent Bottlenecked Attentive Neural ProcessesCode0
Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions0
Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information0
Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice0
Meta-Learning of Neural State-Space Models Using Data From Similar Systems0
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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