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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2201–2225 of 3569 papers

TitleStatusHype
Gaussian Process Meta Few-shot Classifier Learning via Linear Discriminant Laplace Approximation—0
The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning—0
Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image RecognitionCode0
Learning to Rectify for Robust Learning with Noisy LabelsCode0
Meta Cross-Modal Hashing on Long-Tailed Data—0
Crowdsourcing with Meta-Workers: A New Way to Save the Budget—0
MetaMIML: Meta Multi-Instance Multi-Label Learning—0
Meta-Forecasting by combining Global Deep Representations with Local Adaptation—0
Meta-learning for RIS-assisted NOMA Networks—0
Graph neural network initialisation of quantum approximate optimisation—0
A Pixel-Level Meta-Learner for Weakly Supervised Few-Shot Semantic Segmentation—0
Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP—0
Procedural Generalization by Planning with Self-Supervised World Models—0
Robust Dynamic Bus Control: A Distributional Multi-agent Reinforcement Learning Approach—0
Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial AttacksCode0
Spiking Generative Adversarial Networks With a Neural Network Discriminator: Local Training, Bayesian Models, and Continual Meta-Learning—0
Meta-Learning to Improve Pre-Training—0
Influential Prototypical Networks for Few Shot Learning: A Dermatological Case Study—0
Meta-LMTC: Meta-Learning for Large-Scale Multi-Label Text Classification—0
End-to-End Learning of Deep Kernel Acquisition Functions for Bayesian Optimization—0
Beyond Reptile: Meta-Learned Dot-Product Maximization between Gradients for Improved Single-Task Regularization—0
Indic Languages Automatic Speech Recognition using Meta-Learning Approach—0
Meta Distant Transfer Learning for Pre-trained Language Models—0
Few-Shot Named Entity Recognition: An Empirical Baseline Study—0
MetaTS: Meta Teacher-Student Network for Multilingual Sequence Labeling with Minimal Supervision—0
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All datasetsML10MT50ML45OMNIGLOT - 1-Shot, 20-way

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