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 701–750 of 3569 papers

TitleStatusHype
Boosting CLIP Adaptation for Image Quality Assessment via Meta-Prompt Learning and Gradient Regularization—0
A Moreau Envelope Approach for LQR Meta-Policy Estimation—0
Boosting Black-Box Adversarial Attacks with Meta Learning—0
A model-based approach to meta-Reinforcement Learning: Transformers and tree search—0
Adaptive Self-training for Few-shot Neural Sequence Labeling—0
Enhanced Bilevel Optimization via Bregman Distance—0
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation—0
A Model-based Approach for Sample-efficient Multi-task Reinforcement Learning—0
AMM: Adaptive Modularized Reinforcement Model for Multi-city Traffic Signal Control—0
Automatic Self-supervised Learning for Social Recommendations—0
End-to-End Learning of Deep Kernel Acquisition Functions for Bayesian Optimization—0
Black-box Prompt Tuning with Subspace Learning—0
Adaptive Robust Model Predictive Control via Uncertainty Cancellation—0
A Comprehensive Sustainable Framework for Machine Learning and Artificial Intelligence—0
Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks—0
A Meta Understanding of Meta-Learning—0
End to End Generative Meta Curriculum Learning For Medical Data Augmentation—0
End-to-End Offline Speech Translation System for IWSLT 2020 using Modality Agnostic Meta-Learning—0
A Meta-transfer Learning framework for Visually Grounded Compositional Concept Learning—0
BI-MAML: Balanced Incremental Approach for Meta Learning—0
Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift—0
Bilevel Programming for Hyperparameter Optimization and Meta-Learning—0
A Comprehensive Survey of Dataset Distillation—0
Bilevel Optimization for Machine Learning: Algorithm Design and Convergence Analysis—0
Bi-Level Meta-Learning for Few-Shot Domain Generalization—0
A Meta-Reinforcement Learning Approach to Process Control—0
Bilevel Continual Learning—0
Bias-Tolerant Fair Classification—0
A meta learning scheme for fast accent domain expansion in Mandarin speech recognition—0
End-to-End Simultaneous Translation System for IWSLT2020 Using Modality Agnostic Meta-Learning—0
Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning—0
BiAdam: Fast Adaptive Bilevel Optimization Methods—0
Adaptive Resource Allocation for Virtualized Base Stations in O-RAN with Online Learning—0
Beyond Traditional Single Object Tracking: A Survey—0
A Meta-Learning Perspective on Transformers for Causal Language Modeling—0
Enabling Deep Learning-based Physical-layer Secret Key Generation for FDD-OFDM Systems in Multi-Environments—0
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning—0
Beyond Reptile: Meta-Learned Dot-Product Maximization between Gradients for Improved Single-Task Regularization—0
A Meta-Learning Perspective on Cold-Start Recommendations for Items—0
Beyond Induction Heads: In-Context Meta Learning Induces Multi-Phase Circuit Emergence—0
Beyond Exponentially Discounted Sum: Automatic Learning of Return Function—0
Adaptive Reinforcement Learning through Evolving Self-Modifying Neural Networks—0
A Survey on Curriculum Learning—0
Enabling Reproducibility and Meta-learning Through a Lifelong Database of Experiments (LDE)—0
Beyond Bayes-optimality: meta-learning what you know you don't know—0
Betty: An Automatic Differentiation Library for Multilevel Optimization—0
A Meta-learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning—0
BERT Learns to Teach: Knowledge Distillation with Meta Learning—0
Bending the Curve: Improving the ROC Curve Through Error Redistribution—0
A Blockchain-based Reliable Federated Meta-learning for Metaverse: A Dual Game Framework—0
Show:102550
← PrevPage 15 of 72Next →
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