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 1851–1875 of 3569 papers

TitleStatusHype
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
A Pixel-Level Meta-Learner for Weakly Supervised Few-Shot Semantic Segmentation—0
Procedural Generalization by Planning with Self-Supervised World Models—0
Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP—0
Meta-Learning to Improve Pre-Training—0
Spiking Generative Adversarial Networks With a Neural Network Discriminator: Local Training, Bayesian Models, and Continual Meta-Learning—0
Indic Languages Automatic Speech Recognition using Meta-Learning Approach—0
Meta-LMTC: Meta-Learning for Large-Scale Multi-Label Text Classification—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
Beyond Reptile: Meta-Learned Dot-Product Maximization between Gradients for Improved Single-Task Regularization—0
End-to-End Learning of Deep Kernel Acquisition Functions for Bayesian Optimization—0
Influential Prototypical Networks for Few Shot Learning: A Dermatological Case Study—0
RF-Net: a Unified Meta-learning Framework for RF-enabled One-shot Human Activity RecognitionCode1
A Scalable AutoML Approach Based on Graph Neural NetworksCode0
Domain Agnostic Few-Shot Learning For Document Intelligence—0
Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach—0
Learning where to learn: Gradient sparsity in meta and continual learningCode1
Meta-Learning Sparse Implicit Neural RepresentationsCode1
Revisit Multimodal Meta-Learning through the Lens of Multi-Task LearningCode0
Dynamic population-based meta-learning for multi-agent communication with natural language—0
Meta-Knowledge Transfer for Inductive Knowledge Graph EmbeddingCode1
Accelerating Gradient-based Meta Learner—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